INTELLIGENT TAGGING FOR NON-ENGLISH LANGUAGES

 

Intelligent Tagging supports the following non-English languages: Chinese, French, German, Japanese, and Spanish.

While English language support encompasses the full range of Intelligent Tagging metadata types, each of the non-English languages supports a  specific subset of Intelligent Tagging semantic metadata types.

This document provides information about Intelligent Tagging for each of the supported non-English languages, including how to trigger tagging for each of the languages, the list of supported metadata types, and detailed information about the tagging output.

Tagging Chinese Language Documents (Company and Person tagging)

Tagging French Language Documents (Company, Person, MarketIndex, Organization, City, Country, and more.)

Tagging German Language Documents (Company tagging)

Tagging Japanese Language Documents (Company tagging)

Tagging Spanish Language Documents (Company, Person, MarketIndex, Organization, City, Country, and more.)

 

For further information about working with the Intelligent Tagging API, see the API User Guide.

 

 

Tagging Chinese Language Documents

Intelligent Tagging supports Person and Company tagging for Chinese language input texts. 

Supported input text formats are: XML, HTML, PDF, and raw text.

Internal Intelligent Tagging users: Please note that Chinese language processing is supported by the allMetadata profile only.

 

Regarding Company Tagging for Chinese input text:

The reference list of companies includes approximately 8000 of the largest (by market capitalization) public companies traded on the following markets:

  • Shanghai Stock Exchange (China)
  • Shenzhen Stock Exchange (China)
  • Stock Exchange of Hong Kong (Hong Kong)
  • Taiwan Stock Exchange (Taiwan)
  • Taipei Exchange (Taiwan)
  • Nikkei 225
  • MSCI Europe Index
  • STI
  • S&P 500


For more detailed information about working with the Intelligent Tagging API, and about the entire range of Intelligent Tagging metadata output, see the API User Guide.

 

Mandatory Request Headers

Tagging Output

 

 

Mandatory Request Headers

To tag Chinese language input, the API call must define these headers (Internal Intelligent Tagging requires different headers, as described below). Furthermore, we highly recommend using the x-calais-language header for best results.

Header Value
Content-Type Valid values: text/xml; text/raw; text/html; application/pdf.
x-ag-access-token   The value of this header is your license key. (For Intelligent Tagging On Premise, this header is not supported and not relevant.)
x-calais-selectiveTags

Determines which of the supported tagging processes are triggered.

Valid values (not case sensitive): company; person; industry.

You can pass multiple values.

For example, the following triggers company and person tagging: x-calais-selectiveTags:company,person

 

Internal Intelligent Tagging (for internal customers who do not connect through API Gateway) requires the following headers:

Header Value
x-calais-profile allMetadata
Content-Type Valid values: text/xml; text/raw; text/html; application/pdf.
x-calais-licenseID The value of this header is your license key.
x-calais-selectiveTags

Determines which of the supported tagging processes are triggered.

Valid values (not case sensitive): company; person; industry.

You can pass multiple values.

For example, the following triggers company and person tagging: x-calais-selectiveTags:company,person

 

For detailed information about the mandatory headers and also about the optional input headers that can be used to customize the Intelligent Tagging workflow to your use case, see Input Headers.

 

 

Chinese Tagging Output

When using Intelligent Tagging to tag Chinese input text, the following metadata tags may be found in the output:

 

Company entity tag

Company resolution tag

TopmostPublicParentCompany resolution tag

Person entity tag

Instance tag

Relevance tag

Industry tag

 

For a conceptual overview of the metadata tag types, see How Does Intelligent Tagging Work?

 

Chinese Output Examples

Example 1: Company Extraction in JSON Output Format

Example 2: Person Extraction in JSON Output Format

Example 3: Industry Tag in JSON Output Format

Example 4: Company Extraction in RDF Output Format

Example 5: Person Extraction in RDF Output Format

Example 6: Industry Tag in RDF Output Format

 

Example 1: Company Extraction in JSON Output Format

In this output example, we have separated out the individual tags to make them easier to look at. However, please note that in the JSON output, the tags related to the extracted company entity (instance, relevance, and resolution) are nested inside the entity tag.

 

Company entity tag:

    	
            

"http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c": {

        "_typeGroup": "entities",

        "_type": "Company",

        "forenduserdisplay": "true",

        "name": "Xerox Corp",

        "confidencelevel": "1.00",

        "csepermid": "4295905360",

        "_typeReference": "http://s.opencalais.com/1/type/em/e/Company",

Instance tags:

    	
            

 "instances": [

            {

                "detection": "[]施乐[威胁惠普:如果不坐下来谈 我们将实施敌意收购 \nIT业界腾讯科技2019-11-22]",

                "exact": "施乐",

                "suffix": "威胁惠普:如果不坐下来谈 我们将实施敌意收购 \nIT业界腾讯科技2019-11-22",

                "offset": 0,

                "length": 2

            },

            {

                "detection": "[\n收藏 \n[摘要]目前华尔街15名分析师对惠普股票的中位数目标股价是20美元。这一价格明显低于]施乐公司[的收购价。 \n \n \n \n腾讯科技讯 最近,美国老牌科技公司施乐公司提出收购要约,计划斥资335亿美]",

                "prefix": "\n收藏 \n[摘要]目前华尔街15名分析师对惠普股票的中位数目标股价是20美元。这一价格明显低于",

                "exact": "施乐公司",

                "suffix": "的收购价。 \n \n \n \n腾讯科技讯 最近,美国老牌科技公司施乐公司提出收购要约,计划斥资335亿美",

                "offset": 105,

                "length": 4

            },

            {

                "detection": "[\n \n \n \n腾讯科技讯 最近,美国老牌科技公司]施乐公司[提出收购要约,计划斥资335亿美元收购全球个人电脑巨头惠普公司,结果遭到了惠普董事会的拒绝。据外媒最]",

                "prefix": "\n \n \n \n腾讯科技讯 最近,美国老牌科技公司",

                "exact": "施乐公司",

                "suffix": "提出收购要约,计划斥资335亿美元收购全球个人电脑巨头惠普公司,结果遭到了惠普董事会的拒绝。据外媒最",

                "offset": 139,

                "length": 4

           }

        ],

Relevance tag:

    	
             "relevance": 0.8,
        
        
    

Company Resolution tag:

    	
            

 "resolutions": [

            {

                "name": "XEROX CORPORATION",

                "permid": "4295905360",

                "primaryric": "XRX.N",

                "ispublic": "false",

                "commonname": "Xerox",

                "score": 1,

                "id": "https://permid.org/1-4295905360",

                "ticker": "XRX"

            },

TopmostPublicParentCompany Resolution tag:

    	
            

 {

                "name": "XEROX HOLDINGS CORPORATION",

                "permid": "5068338327",

                "primaryric": "XRX.N",

                "ispublic": "true",

                "commonname": "Xerox Hlngs",

                "topmostPublicParent": true,

                "id": "https://permid.org/1-5068338327"

            }

 

Example 2: Person Extraction in JSON Output Format

Note that the tags related to the extracted person entity (instance and relevance) are nested inside the Person entity tag.

Person entity tag:

    	
            

"http://d.opencalais.com/pershash-1/343d4b5d-f717-33eb-b892-5bcec4c3560f": {

        "_typeGroup": "entities",

        "_type": "Person",

        "forenduserdisplay": "true",

        "name": "習近平",

        "firstname": "N/A",

        "lastname": "N/A",

        "persontype": "N/A",

        "nationality": "N/A",

        "confidencelevel": "0.9891722",

        "commonname": "習近平",

        "_typeReference": "http://s.opencalais.com/1/type/em/e/Person",

        "permid": "https://permid.org/1-404011",

        "instances": [

            {

                "detection": "[\n \n圖片版權AFP \n直到去年12月在阿根廷,特朗普和]習近平[坐下來,說好了先休戰,兩邊派人開始談判,第二季的劇情的大幕拉開。 \n]",

                "prefix": "\n \n圖片版權AFP \n直到去年12月在阿根廷,特朗普和",

                "exact": "習近平",

                "suffix": "坐下來,說好了先休戰,兩邊派人開始談判,第二季的劇情的大幕拉開。 \n",

                "offset": 680,

                "length": 3

            },

            {

                "detection": "[\n \n這場大戲還有第三季。今年6月底,特朗普和]習近平[在日本大阪出席G20峰會期間再次坐到一起,兩人同意再次開啟談判。於是,7月底,兩國在上海再次開啟第十]",

                "prefix": "\n \n這場大戲還有第三季。今年6月底,特朗普和",

                "exact": "習近平",

                "suffix": "在日本大阪出席G20峰會期間再次坐到一起,兩人同意再次開啟談判。於是,7月底,兩國在上海再次開啟第十",

                "offset": 924,

                "length": 3

            }

        ],

        "relevance": 0.2

 

Example 3: Industry Tag in JSON Output Format

    	
            

"http://d.opencalais.com/dochash-1/bfb209f6-e3d5-3d8f-9aa6-04693fa3302e/Industry/1": {

        "_typeGroup": "industry",

        "forenduserdisplay": "false",

        "name": "Wireless Telecommunications Services - NEC",

        "rcscode": "B:1817",

        "trbccode": "5810102010",

        "permid": "4294951184",

        "relevance": 0.2

    },

 

 

 

Example 4: Company Extraction in RDF Output Format

Intelligent Tagging assigns a unique ID (a hash tag) to the extracted entity. In this example, the hash tag for the extracted entity, Xerox Corporation, is  comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c.

In the RDF output format, the same hash tag is displayed by the "subject" attribute of all the instance tags that identify mentions of Xerox Corporation, linking them to the extracted entity and to each other. Likewise, the "subject" attribute of the related Relevance and Resolution tags also display the same hash tag.

(This is not relevant to the JSON output which nests all related tags within the entity markup tag.) 

Tip: Since the Intelligent Tagging output does not group metadata types, we highly recommend using the CalaisModel Abstraction Layer to simplify parsing the Intelligent Tagging RDF output. The Abstraction Layer is relevant to the RDF/XML response format only. For further information, see the Abstraction Layer Developer Guide. Abstraction Layer libraries are available on the Downloads tab.

 

Company entity markup tag (em/e/Company):

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/em/e/Company"/>

        <c:forenduserdisplay>true</c:forenduserdisplay>

        <c:name>Xerox Corp</c:name>

        <c:confidencelevel>1.00</c:confidencelevel>

        <c:csepermid>4295905360</c:csepermid>

    </rdf:Description>

InstanceInfo tags:

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/66d60c3e-be61-36e0-a113-eca7b4c6775c/Instance/2">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/InstanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/66d60c3e-be61-36e0-a113-eca7b4c6775c"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <!--Company: Xerox Corp; -->

        <c:detection>[]施乐[威胁惠普:如果不坐下来谈 我们将实施敌意收购 

IT业界腾讯科技2019-11-22]</c:detection>

        <c:prefix/>

        <c:exact>施乐</c:exact>

        <c:suffix>威胁惠普:如果不坐下来谈 我们将实施敌意收购 

IT业界腾讯科技2019-11-22</c:suffix>

        <c:offset>0</c:offset>

        <c:length>2</c:length>

    </rdf:Description>

    	
            

  <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/66d60c3e-be61-36e0-a113-eca7b4c6775c/Instance/3">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/InstanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/66d60c3e-be61-36e0-a113-eca7b4c6775c"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <!--Company: Xerox Corp; -->

        <c:detection>[

收藏 

[摘要]目前华尔街15名分析师对惠普股票的中位数目标股价是20美元。这一价格明显低于]施乐公司[的收购价。 

 

 

 

腾讯科技讯 最近,美国老牌科技公司施乐公司提出收购要约,计划斥资335亿美]</c:detection>

        <c:prefix>

收藏 

[摘要]目前华尔街15名分析师对惠普股票的中位数目标股价是20美元。这一价格明显低于</c:prefix>

        <c:exact>施乐公司</c:exact>

        <c:suffix>的收购价。 

 

 

 

腾讯科技讯 最近,美国老牌科技公司施乐公司提出收购要约,计划斥资335亿美</c:suffix>

        <c:offset>105</c:offset>

        <c:length>4</c:length>

    </rdf:Description>

 

RelevanceInfo tag:

    	
            

<rdf:Description rdf:about="http://d.opencalais.com/dochash-1/66d60c3e-be61-36e0-a113-eca7b4c6775c/Relevance/2">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/RelevanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/66d60c3e-be61-36e0-a113-eca7b4c6775c"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <c:relevance>0.8</c:relevance>

        <c:relevancecont>0.84</c:relevancecont>

    </rdf:Description>

Company Resolution Tag (er/Company):

    	
            

<rdf:Description rdf:about="http://d.opencalais.com/er/company/ralg-oa/4295905360">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/er/Company"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/66d60c3e-be61-36e0-a113-eca7b4c6775c"/>

        <c:name>XEROX CORPORATION</c:name>

        <c:permid>4295905360</c:permid>

        <c:primaryric>XRX.N</c:primaryric>

        <c:ispublic>false</c:ispublic>

        <c:commonname>Xerox</c:commonname>

        <c:score>1.0</c:score>

        <!--Xerox Corp-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <c:legacyid rdf:resource="http://d.opencalais.com/er/company/ralg-tr1r/59dbf5cc-ca27-3463-a820-7e1c9bca78e3"/>

        <c:openpermid rdf:resource="https://permid.org/1-4295905360"/>

        <c:ticker>XRX</c:ticker>

    </rdf:Description>

TopmostPublicParentCompany Resolution Tag (er/topmostPublicParentCompany):

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/er/topmostpublicparentcompany/ralg-oa-tm/5068338327">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/er/TopmostPublicParentCompany"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/66d60c3e-be61-36e0-a113-eca7b4c6775c"/>

        <c:name>XEROX HOLDINGS CORPORATION</c:name>

        <c:permid>5068338327</c:permid>

        <c:primaryric>XRX.N</c:primaryric>

        <c:ispublic>true</c:ispublic>

        <c:commonname>Xerox Hlngs</c:commonname>

        <!--Xerox Corp-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <c:legacyid rdf:resource="http://d.opencalais.com/er/company/ralg-tr1r/922d30f3-4d37-365e-9c84-a6c9a8974f8e"/>

        <c:openpermid rdf:resource="https://permid.org/1-5068338327"/>

    </rdf:Description>

 

 

 

Example 5: Person Extraction in RDF Output Format

Please note that the person hash tag, pershash-1/9ca3aae9-dac8-3f49-bf78-332dcd6358a1, is displayed by the "subject" attribute of all the instance tags that identify mentions of 马永哲, linking them to the extracted entity and to each other. Likewise, the "subject" attribute of the related Relevance tag also displays the same hash tag.

Person entity markup tag:

    	
            

  <rdf:Description rdf:about="http://d.opencalais.com/pershash-1/9ca3aae9-dac8-3f49-bf78-332dcd6358a1">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/em/e/Person"/>

        <c:permid rdf:resource="https://permid.org/1-404011"/>

        <c:forenduserdisplay>true</c:forenduserdisplay>

        <c:name>马永哲</c:name>

        <c:firstname>N/A</c:firstname>

        <c:lastname>N/A</c:lastname>

        <c:persontype>N/A</c:persontype>

        <c:nationality>N/A</c:nationality>

        <c:confidencelevel>0.9761618</c:confidencelevel>

        <c:commonname>马永哲</c:commonname>

    </rdf:Description>

InstanceInfo tags:

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/d9fc33cf-a154-3482-9386-3ded9cdc0db0/Instance/7">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/InstanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/d9fc33cf-a154-3482-9386-3ded9cdc0db0"/>

        <c:subject rdf:resource="http://d.opencalais.com/pershash-1/9ca3aae9-dac8-3f49-bf78-332dcd6358a1"/>

        <!--Person: 马永哲; -->

        <c:detection>[讯等国内实力强大的玩家,它们已经让中国央行的大部分现金没有可用之地,”美国彼得森国际经济研究所研究员]马永哲[(Martin Chorzempa)对BBC说。 

]</c:detection>

        <c:prefix>讯等国内实力强大的玩家,它们已经让中国央行的大部分现金没有可用之地,”美国彼得森国际经济研究所研究员</c:prefix>

        <c:exact>马永哲</c:exact>

        <c:suffix>(Martin Chorzempa)对BBC说。 

</c:suffix>

        <c:offset>2090</c:offset>

        <c:length>3</c:length>

    </rdf:Description>

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/d9fc33cf-a154-3482-9386-3ded9cdc0db0/Instance/8">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/InstanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/d9fc33cf-a154-3482-9386-3ded9cdc0db0"/>

        <c:subject rdf:resource="http://d.opencalais.com/pershash-1/9ca3aae9-dac8-3f49-bf78-332dcd6358a1"/>

        <!--Person: 马永哲; -->

        <c:detection>[实力强大的玩家,它们已经让中国央行的大部分现金没有可用之地,”美国彼得森国际经济研究所研究员马永哲(]Martin Chorzempa[)对BBC说。 

 

中国人民银行在最近的一份报告中称,截至2018年末,中国使用电子支付的成年人比]</c:detection>

        <c:prefix>实力强大的玩家,它们已经让中国央行的大部分现金没有可用之地,”美国彼得森国际经济研究所研究员马永哲(</c:prefix>

        <c:exact>Martin Chorzempa</c:exact>

        <c:suffix>)对BBC说。 

 

中国人民银行在最近的一份报告中称,截至2018年末,中国使用电子支付的成年人比</c:suffix>

        <c:offset>2094</c:offset>

        <c:length>16</c:length>

    </rdf:Description>

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/d9fc33cf-a154-3482-9386-3ded9cdc0db0/Instance/9">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/InstanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/d9fc33cf-a154-3482-9386-3ded9cdc0db0"/>

        <c:subject rdf:resource="http://d.opencalais.com/pershash-1/9ca3aae9-dac8-3f49-bf78-332dcd6358a1"/>

        <!--Person: 马永哲; -->

        <c:detection>[

 

答案是:不确定,但至少不是唯一选择。 

 

]马永哲[对BBC解释称,区块链技术不太可能处于这个全国性数字货币系统的核心,因为其“效率太低了”。 

]</c:detection>

        <c:prefix>

 

答案是:不确定,但至少不是唯一选择。 

 

</c:prefix>

        <c:exact>马永哲</c:exact>

        <c:suffix>对BBC解释称,区块链技术不太可能处于这个全国性数字货币系统的核心,因为其“效率太低了”。 

</c:suffix>

        <c:offset>2568</c:offset>

        <c:length>3</c:length>

    </rdf:Description>

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/d9fc33cf-a154-3482-9386-3ded9cdc0db0/Instance/10">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/InstanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/d9fc33cf-a154-3482-9386-3ded9cdc0db0"/>

        <c:subject rdf:resource="http://d.opencalais.com/pershash-1/9ca3aae9-dac8-3f49-bf78-332dcd6358a1"/>

        <!--Person: 马永哲; -->

        <c:detection>[,没有政府能够控制或有效禁止。在党的定义下,它更可能的目的是将其用来帮助政府部门和公司之间的合作,”]马永哲[说。 

 

相关主题内容]</c:detection>

        <c:prefix>,没有政府能够控制或有效禁止。在党的定义下,它更可能的目的是将其用来帮助政府部门和公司之间的合作,”</c:prefix>

        <c:exact>马永哲</c:exact>

        <c:suffix>说。 

 

相关主题内容</c:suffix>

        <c:offset>2838</c:offset>

        <c:length>3</c:length>

    </rdf:Description>

 

RelevanceInfo tag:

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/d9fc33cf-a154-3482-9386-3ded9cdc0db0/Relevance/4">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/RelevanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/d9fc33cf-a154-3482-9386-3ded9cdc0db0"/>

        <c:subject rdf:resource="http://d.opencalais.com/pershash-1/9ca3aae9-dac8-3f49-bf78-332dcd6358a1"/>

        <c:relevance>0.2</c:relevance>

    </rdf:Description>

 

Example 6: Industry Tag in RDF Output Format

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/d9fc33cf-a154-3482-9386-3ded9cdc0db0/Industry/2">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/tag/Industry"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/d9fc33cf-a154-3482-9386-3ded9cdc0db0"/>

        <c:forenduserdisplay>false</c:forenduserdisplay>

        <c:name>Internet Services - NEC</c:name>

        <c:rcscode>B:1804</c:rcscode>

        <c:trbccode>5720103010</c:trbccode>

        <c:permid>4294951197</c:permid>

        <c:relevance>0.200</c:relevance>

    </rdf:Description>

 

Tagging French Language Documents

Itelligent Tagging can extract the following entities from French input text: City, Company, Continent, Country, EmailAddress, FaxNumber, MarketIndex, NaturalFeature, Organization, Person, PhoneNumber, ProvinceOrState, Region, URL.

 

The tagging output may include any of these tags:

 

For a conceptual overview of the Intelligent Tagging Semantic Metadata Tags, see How Does Intelligent Tagging Work?

 

Mandatory Request Headers

Output Examples

 

Mandatory Request Headers

To tag French language input, the API call must define these headers (Internal Intelligent Tagging requires different headers, as described below). Furthermore, we highly recommend using the x-calais-language header for best results.

Header Value
Content-Type Valid values: text/xml; text/raw; text/html; application/pdf.
x-ag-access-token The value of this header is your license key. (For Intelligent Tagging On Premise, this header is not supported and not relevant.)
x-calais-selectiveTags

Determines which of the supported tagging processes are triggered.

Valid values (not case sensitive): city; company; continent; country; emailAddress; faxNumber; marketIndex; naturalFeature; organization; person; phoneNumber; provinceOrState; region; url; industry.

You can pass multiple values.

For example, the following triggers city, company, and person tagging: x-calais-selectiveTags:city,company,person

 

Internal Intelligent Tagging (for internal customers who do not connect through API Gateway) requires the following headers:

Header Value
x-calais-profile allMetadata
Content-Type Valid values: text/xml; text/raw; text/html; application/pdf.
x-calais-licenseID The value of this header is your license key.
x-calais-selectiveTags

Determines which of the supported tagging processes are triggered.

Valid values (not case sensitive): city; company; continent; country; emailAddress; faxNumber; marketIndex; naturalFeature; organization; person; phoneNumber; provinceOrState; region; url; industry.

You can pass multiple values.

For example, the following triggers city, company, and person tagging: x-calais-selectiveTags:city,company,person

 

For detailed information about the mandatory headers and also about the optional input headers that can be used to customize the Intelligent Tagging workflow to your use case, see Input Headers.

 

French Output Examples

Example 1: Company Extraction in JSON Output Format

Example 2: Person Extraction in JSON Output Format

Example 3: City Extraction in JSON Output Format

Example 4: Industry Tag in JSON Output Format

Example 5: Company Extraction in RDF Output Format

Example 6: Person Extraction in RDF Output Format

Example 7: Industry Tag in RDF Output Format

Additional Examples for all supported entity types

 

 

Example 1: Company Extraction in JSON Output Format

In this output example, we have separated out the individual tags to make them easier to look at. However, please note that in the JSON output, related tags (such as Instance, Relevance, and Resolution) are nested inside the entity tag.

 

Company entity markup tag:

    	
            

 "http://d.opencalais.com/comphash-1/f6bbc98e-8c5b-3e3b-9253-8e3aeaa3058c": {

        "_typeGroup": "entities",

        "_type": "Company",

        "forenduserdisplay": "true",

        "name": "Xerox",

        "confidencelevel": "0.906",

        "recognizedas": "name",

      

 

 

Confidence tag:

    	
            

  "confidence": {

            "statisticalfeature": "0.837",

            "dblookup": "0.0",

            "resolution": "0.9223276",

            "aggregate": "0.906"

        },

       

 

Company Resolution tag:

    	
            

 "resolutions": [

            {

                "name": "XEROX CORPORATION",

                "permid": "4295905360",

                "primaryric": "XRX.N",

                "ispublic": "false",

                "commonname": "Xerox",

                "score": 0.9223276,

                "id": "https://permid.org/1-4295905360",

                "ticker": "XRX"

            },

          

 

topmostPublicParent resolution tag:

    	
            

  {

                "name": "XEROX HOLDINGS CORPORATION",

                "permid": "5068338327",

                "primaryric": "XRX.N",

                "ispublic": "true",

                "commonname": "Xerox Hlngs",

                "topmostPublicParent": true,

                "id": "https://permid.org/1-5068338327"

            }

        ],

     

Instance tags:

    	
            

   "_typeReference": "http://s.opencalais.com/1/type/em/e/Company",

        "instances": [

            {

                "detection": "[]Xerox[ envisagerait de racheter HP \nSelon le « Wall]",

                "exact": "Xerox",

                "suffix": " envisagerait de racheter HP \nSelon le « Wall",

                "offset": 0,

                "length": 5

            },

            {

                "detection": "[2019 à 8h54 \nSelon le « Wall Street Journal », ]Xerox[ réfléchit à une offre de rachat d'actions pour]",

                "prefix": "2019 à 8h54 \nSelon le « Wall Street Journal », ",

                "exact": "Xerox",

                "suffix": " réfléchit à une offre de rachat d'actions pour",

                "offset": 671,

                "length": 5

            },

            {

                "detection": "[de trois fois la capitalisation boursière de ]Xerox[. \n \nCe rachat permettrait aux deux fleurons de]",

                "prefix": "de trois fois la capitalisation boursière de ",

                "exact": "Xerox",

                "suffix": ". \n \nCe rachat permettrait aux deux fleurons de",

                "offset": 933,

                "length": 5

            },

            {

                "detection": "[américain. Le conseil d'administration de ]Xerox[ s'est réuni mardi pour étudier une proposition]",

                "prefix": "américain. Le conseil d'administration de ",

                "exact": "Xerox",

                "suffix": " s'est réuni mardi pour étudier une proposition",

                "offset": 1163,

                "length": 5

            },

            {

                "detection": "[d'accord. Toujours selon les mêmes sources, ]Xerox[ aurait déjà un engagement de financement]",

                "prefix": "d'accord. Toujours selon les mêmes sources, ",

                "exact": "Xerox",

                "suffix": " aurait déjà un engagement de financement",

                "offset": 1260,

                "length": 5

            },

            {

                "detection": "[\n \nRécupérer des parts de marché \nMardi, ]Xerox[ a vendu pour 2,3 milliards de dollars sa]",

                "prefix": "\n \nRécupérer des parts de marché \nMardi, ",

                "exact": "Xerox",

                "suffix": " a vendu pour 2,3 milliards de dollars sa",

                "offset": 1516,

                "length": 5

            },

            {

                "detection": "[de dollars sa participation de 25 % dans Fuji ]Xerox[ . Et s'est évité un procès avec l'entreprise]",

                "prefix": "de dollars sa participation de 25 % dans Fuji ",

                "exact": "Xerox",

                "suffix": " . Et s'est évité un procès avec l'entreprise",

                "offset": 1595,

                "length": 5

            }

        ],

       

Relevance tag:

    	
             "relevance": 0.8
        
        
    

 

Example 2: Person Extraction in JSON Output Format

Note that the related tags (confidence, instance, and relevance) are nested inside the Person entity tag.

Person entity extraction:

    	
            

 "http://d.opencalais.com/pershash-1/4bc0460f-cc99-3b30-ab49-70f9e153e2c3": {

        "_typeGroup": "entities",

        "_type": "Person",

        "forenduserdisplay": "true",

        "firstname": "Emmanuel",

        "lastname": "Macron",

        "nationality": "N/A",

        "persontype": "N/A",

        "name": "Emmanuel Macron",

        "confidencelevel": "0.407",

        "commonname": "Emmanuel Macron",

        "confidence": {

            "statisticalfeature": "0.507",

            "dblookup": "0.0",

            "resolution": "0.0",

            "aggregate": "0.407"

        },

        "_typeReference": "http://s.opencalais.com/1/type/em/e/Person",

        "permid": "https://permid.org/1-404011",

        "instances": [

            {

                "detection": "[possibles et au-delà », a déclaré le président ]Emmanuel Macron[, après avoir abordé la question avec la]",

                "prefix": "possibles et au-delà », a déclaré le président ",

                "exact": "Emmanuel Macron",

                "suffix": ", après avoir abordé la question avec la",

                "offset": 222,

                "length": 15

            }

        ],

        "relevance": 0.2

    }

}

 

Example 3: City Extraction in JSON Output Format

    	
            

 "http://d.opencalais.com/genericHasher-1/5ba6ff2e-c0c3-344f-b63e-9673a1b3df2e": {

        "_typeGroup": "entities",

        "_type": "City",

        "forenduserdisplay": "true",

        "name": "Toulouse",

        "_typeReference": "http://s.opencalais.com/1/type/em/e/City",

        "instances": [

            {

                "detection": "[franco-allemand qui s’est tenu mi-octobre à ]Toulouse[. Il a, en outre, proposé de développer « de]",

                "prefix": "franco-allemand qui s’est tenu mi-octobre à ",

                "exact": "Toulouse",

                "suffix": ". Il a, en outre, proposé de développer « de",

                "offset": 360,

                "length": 8

            }

        ],

        "relevance": 0.2,

        "resolutions": [

            {

                "name": "Toulouse",

                "shortname": "Toulouse",

                "latitude": "43.6053",

                "longitude": "1.4428"

            }

 

Example 4: Industry Tag in JSON Output Format

    	
            

 "http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27/Industry/1": {

        "_typeGroup": "industry",

        "forenduserdisplay": "false",

        "name": "Office Equipment - NEC",

        "rcscode": "B:1779",

        "trbccode": "5710501010",

        "permid": "4294951222",

        "relevance": 0.2

    },

 

Example 5: Company Extraction in RDF Output Format

Intelligent Tagging assigns a unique ID (a hash tag) to the extracted entity. In this example, the hash tag for the extracted entity, Xerox, is  comphash-1/f6bbc98e-8c5b-3e3b-9253-8e3aeaa3058c.

In the RDF output format, the same hash tag is displayed by the "subject" attribute of all the instance tags that identify mentions of Xerox, linking them to the extracted entity and to each other. Likewise, the "subject" attribute of the related Confidence, Relevance, and Resolution tags also display the same hash tag.

(This is not relevant to the JSON output which nests all related tags within the entity markup tag.) 

Tip: Since the Intelligent Tagging output does not group metadata types, we highly recommend using the CalaisModel Abstraction Layer to simplify parsing the Intelligent Tagging RDF output. The Abstraction Layer is relevant to the RDF/XML response format only. For further information, see the Abstraction Layer Developer Guide. Abstraction Layer libraries are available on the Downloads tab.

Company entity markup tag (em/e/Company):

    	
            

  <rdf:Description rdf:about="http://d.opencalais.com/comphash-1/f6bbc98e-8c5b-3e3b-9253-8e3aeaa3058c">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/em/e/Company"/>

        <c:forenduserdisplay>true</c:forenduserdisplay>

        <c:name>Xerox</c:name>

        <c:confidencelevel>0.906</c:confidencelevel>

        <c:recognizedas>name</c:recognizedas>

    </rdf:Description>

 

InstanceInfo tags:

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27/Instance/17">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/InstanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/f6bbc98e-8c5b-3e3b-9253-8e3aeaa3058c"/>

        <!--Company: Xerox; -->

        <c:detection>[]Xerox[ envisagerait de racheter HP 

Selon le « Wall]</c:detection>

        <c:prefix/>

        <c:exact>Xerox</c:exact>

        <c:suffix> envisagerait de racheter HP 

Selon le « Wall</c:suffix>

        <c:offset>0</c:offset>

        <c:length>5</c:length>

    </rdf:Description>

    	
            

  <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27/Instance/19">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/InstanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/f6bbc98e-8c5b-3e3b-9253-8e3aeaa3058c"/>

        <!--Company: Xerox; -->

        <c:detection>[de trois fois la capitalisation boursière de ]Xerox[. 

 

Ce rachat permettrait aux deux fleurons de]</c:detection>

        <c:prefix>de trois fois la capitalisation boursière de </c:prefix>

        <c:exact>Xerox</c:exact>

        <c:suffix>. 

 

Ce rachat permettrait aux deux fleurons de</c:suffix>

        <c:offset>933</c:offset>

        <c:length>5</c:length>

    </rdf:Description>

    	
            

  <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27/Instance/21">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/InstanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/f6bbc98e-8c5b-3e3b-9253-8e3aeaa3058c"/>

        <!--Company: Xerox; -->

        <c:detection>[d'accord. Toujours selon les mêmes sources, ]Xerox[ aurait déjà un engagement de financement]</c:detection>

        <c:prefix>d'accord. Toujours selon les mêmes sources, </c:prefix>

        <c:exact>Xerox</c:exact>

        <c:suffix> aurait déjà un engagement de financement</c:suffix>

        <c:offset>1260</c:offset>

        <c:length>5</c:length>

    </rdf:Description>

Confidence tag:

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/conf/comphash-1/f6bbc98e-8c5b-3e3b-9253-8e3aeaa3058c">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/tag/Confidence"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27"/>

        <!--Xerox-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/f6bbc98e-8c5b-3e3b-9253-8e3aeaa3058c"/>

        <c:statisticalfeature>0.837</c:statisticalfeature>

        <c:dblookup>0.0</c:dblookup>

        <c:resolution>0.9223276</c:resolution>

        <c:aggregate>0.906</c:aggregate>

    </rdf:Description>

 

RelevanceInfo tag:

    	
            

<rdf:Description rdf:about="http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27/Relevance/7">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/RelevanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/f6bbc98e-8c5b-3e3b-9253-8e3aeaa3058c"/>

        <c:relevance>0.8</c:relevance>

        <c:relevancecont>0.82</c:relevancecont>

    </rdf:Description>


Company Resolution tag (er/Company):

    	
            

<rdf:Description rdf:about="http://d.opencalais.com/er/company/ralg-oa/4295905360">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/er/Company"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27"/>

        <c:name>XEROX CORPORATION</c:name>

        <c:permid>4295905360</c:permid>

        <c:primaryric>XRX.N</c:primaryric>

        <c:ispublic>false</c:ispublic>

        <c:commonname>Xerox</c:commonname>

        <c:score>0.9223276</c:score>

        <!--Xerox-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/f6bbc98e-8c5b-3e3b-9253-8e3aeaa3058c"/>

        <c:legacyid rdf:resource="http://d.opencalais.com/er/company/ralg-tr1r/59dbf5cc-ca27-3463-a820-7e1c9bca78e3"/>

        <c:openpermid rdf:resource="https://permid.org/1-4295905360"/>

        <c:ticker>XRX</c:ticker>

    </rdf:Description>

TopmostPublicParentCompany:

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/er/topmostpublicparentcompany/ralg-oa-tm/5068338327">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/er/TopmostPublicParentCompany"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27"/>

        <c:name>XEROX HOLDINGS CORPORATION</c:name>

        <c:permid>5068338327</c:permid>

        <c:primaryric>XRX.N</c:primaryric>

        <c:ispublic>true</c:ispublic>

        <c:commonname>Xerox Hlngs</c:commonname>

        <!--Xerox-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/f6bbc98e-8c5b-3e3b-9253-8e3aeaa3058c"/>

        <c:legacyid rdf:resource="http://d.opencalais.com/er/company/ralg-tr1r/922d30f3-4d37-365e-9c84-a6c9a8974f8e"/>

        <c:openpermid rdf:resource="https://permid.org/1-5068338327"/>

    </rdf:Description>

 

Example 6: Person Entity Tag in RDF Output Format

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/pershash-1/0437d6e1-24f9-363e-a0c6-a43955536258">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/em/e/Person"/>

        <c:permid rdf:resource="https://permid.org/1-404011"/>

        <c:forenduserdisplay>true</c:forenduserdisplay>

        <c:persontype>N/A</c:persontype>

        <c:confidencelevel>0.762</c:confidencelevel>

        <c:firstname>Justin</c:firstname>

        <c:name>Justin Sullivan</c:name>

        <c:lastname>Sullivan</c:lastname>

        <c:nationality>N/A</c:nationality>

        <c:commonname>Justin Sullivan</c:commonname>

    </rdf:Description>

 

Example 7: Industry Tag in RDF Output Format

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27/Industry/1">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/tag/Industry"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/0b99ca8c-77c5-3b8f-a882-cb7814e5bb27"/>

        <c:forenduserdisplay>false</c:forenduserdisplay>

        <c:name>Office Equipment - NEC</c:name>

        <c:rcscode>B:1779</c:rcscode>

        <c:trbccode>5710501010</c:trbccode>

        <c:permid>4294951222</c:permid>

        <c:relevance>0.200</c:relevance>

    </rdf:Description>

 

Additional Examples for all Supported Entity Types

City Entity

Input Text: Il y a 3 jours, elle était à Cannes où elle s'était produite au Palais Club.

Extracted Entity: Cannes

Input Text: Jacques Chirac, vêtu comme un estivant et détendu comme un touriste, visite Saint-Tropez.

Extracted Entity: Saint-Tropez

 

Company Entity

Input Text: MONTREAL - La société Clarke (TSX:CKI), basée à Halifax, a présenté une offre pour racheter la compagnie montréalaise de vidéos et de musique Madacy Entertainment (TSX: MEG.UN) et en fermer le capital.

Extracted Entity: Clarke

Extracted Entity: Madacy Entertainment

 

Input Text: A Xingtai, dans la province d'Hebei, la société Jinniu Energya incinéré quelque 1.200 tonnes de lait en poudre à plus de 1.800 degrés le mois dernier.

Extracted Entity: Jinniu Energya

 

Continent Entity

Input Text: L'Allemagne reste le premier partenaire commercial de la Chine en Europe.

Extracted Entity: Europe

 

Country Entity

Input Text: La Russie a hier confirmé à envoyer des renforts aux secteurs occupés comprenant les munitions et le personnel.

Extracted Entity: Russie

 

Input Text: L'ancien Beatles, Paul McCartney, est parti en voyage aux États-Unis avec sa compagne, Nancy Shevell.

Extracted Entity: États-Unis

 

EmailAddress Entity

Input Text: Les demandes d'autorisation doivent être adressées par courrier électronique à publications@wto.org.

Extracted Entity: publications@wto.org 

 

FaxNumber  Entity

Input Text: Le Bureau Langelier du SCC, au téléphone (514) 493-0995 et au télécopieur (514) 493-3306, assure la liaison.

Extracted Entity: (514) 493-3306

 

MarketIndex Entity

Input Text: Le Nasdaq a glissé de 46,13 points à 1505,9, tandis que le S&P 500 a reculé de 27,85 points à 848,92.

Extracted Entity:  NASDAQ 100

Extracted Entity: S&P 500

 

NaturalFeature Entity

Input Text: Sur les pentes du mont Solisko, plusieurs pistes offrent d'excellentes conditions de ski alpin pour tous niveaux.

Extracted Entity: mont Solisko

 

Input Text: Les pêcheurs avaient l'habitude de migrer le long des berges et vers les lacs Habaniya et Tharthar, qui se trouvent en zone sunnite.

Extracted Entity: lac Habaniya

Extracted Entity: lac Tharthar

 

Organization Entity

Input Text: M. Obasanjo doit s'entretenir samedi matin à Kinshasa avec les ambassadeurs des pays membres du Conseil de sécurité de l'ONU en poste en RDC.

Extracted Entity: Conseil de sécurité de l'ONU

 

Person Entity

Input Text: De son côté, le Premier secrétaire sortant François Hollande s'efforçait de dédramatiser.

Extracted Entity: François Hollande

 

Input Text: Sur le terrain de West Brom, Jose Bosingwa a ouvert le score avant qu'Anelka ne trouve le chemin des filets deux fois en l'espace de 13 minutes.

Extracted Entity: Jose Bosingwa

 

PhoneNumber Entity

Input Text: Les billets pour ce spectacle seront disponibles au coût de 45,50$ à 59,50$ au Grand Théâtre, par téléphone au (418) 643-8131 ou au 1 877 643-8131 ainsi que par l'entremise du réseau Billetech.

Extracted Entity: (418) 643-8131

Extracted Entity: 1 877 643-8131

 

ProvinceOrState Entity

Input Text: Accompagné du général Jacques Grandchamp, commandant de la région de gendarmerie de Rhône-Alpes, M. Gaudin a souligné que, jusqu'à présent, aucun élément ne tendait vers autre chose qu'une disparition "accidentelle".

Extracted Entity: Rhône-Alpes

 

Region Entity

Input Text: 
DJAVA, Géorgie - La Géorgie a lancé vendredi une offensive militaire de grande envergure pour reprendre le contrôle de l'Ossétie du Sud, dans le nord du pay.

Extracted Entity: Ossétie du Sud

 

URL Entity

Input Text: Les billets seront mis en vente samedi le 15 novembre à la billetterie de la Place des Arts, par Internet à www.pda.qc.ca.

Extracted Entity: www.pda.qc.ca

 

 

Tagging German Language Documents  

Intelligent Tagging supports extraction of companies from German input text.

The reference list of companies includes approximately 1700 companies traded on the U.S., German, Swiss, and Austrian markets. 

Notes:

  • German language processing is tested on XML input.
  • Internal Intelligent Tagging users: German language processing is supported by the allMetadata profile only.

 

Mandatory Request Headers

Tagging Output

 

Mandatory Request Headers

To tag German language input, the API call must define these headers (Internal Intelligent Tagging requires different headers, as described below). Furthermore, we highly recommend using the x-calais-language header for best results.

Header Value
Content-Type text/xml
x-ag-access-token The value of this header is your license key. (For Intelligent Tagging On Premise, this header is not supported and not relevant.)
x-calais-selectiveTags

Determines which of the supported tagging processes are triggered.

Valid values (not case sensitive): company; industry.

You can pass multiple values.

For example: x-calais-selectiveTags:company,industry

 

Internal Intelligent Tagging (for internal customers who do not connect through API Gateway) requires the following headers:

Header Value
x-calais-profile allMetadata
Content-Type text/xml
x-calais-licenseID The value of this header is your license key.
x-calais-selectiveTags

Determines which of the supported tagging processes are triggered. 

Valid values (jnot case sensitive): company; industry.

You can pass multiple values.

For example: x-calais-selectiveTags:company,industry

 

For detailed information about the mandatory headers and also about the optional input headers that can be used to customize the Intelligent Tagging workflow to your use case, see Input Headers.

 

 

German Tagging Output

When using Intelligent Tagging to extract companies from German input text, the following metadata tags may be found in the output:

Instance tag

Company entity markup tag

Relevance tag

Confidence Tag

Company resolution tag

TopmostPublicParentCompany resolution tag

Industry tag

 

For a conceptual overview of the Intelligent Tagging Semantic Metadata Tags, see How Does Intelligent Tagging Work?

 

German Output Examples

Example 1: Company Extraction in JSON Output Format (Alphabet Inc)

Example 2: Company Extraction in JSON Output Format (Xerox Corp)

Example 3: Company Extraction in JSON Output Format (Apple Inc)

Example 4: Industry Tag in JSON Output Format

Example 5: Company Extraction in RDF Output Format

Example 6: Industry Tag in RDF Output Format


 

Example 1: Company Extraction in JSON Output Format (Xerox Corp)

In this output example, we have separated out the individual tags to make them easier to look at. However, please note that in the JSON output, related tags (such as Instance, relevance, resolution, and confidence) are nested inside the entity tag.

 

Company entity markup tag:

    	
            

 "http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c": {

        "_typeGroup": "entities",

        "_type": "Company",

        "forenduserdisplay": "true",

        "name": "Xerox Corp",

        "confidencelevel": "0.868",

        "csepermid": "4295905360",

       

 

Confidence tag:

    	
            

 "confidence": {

            "statisticalfeature": "0.668",

            "dblookup": "0.0",

            "resolution": "1.0",

            "aggregate": "0.868"

        },

       

 

Instance tag:

    	
            

 "_typeReference": "http://s.opencalais.com/1/type/em/e/Company",

        "instances": [

            {

                "detection": "[\n<Title> Mögliche Elefantenhochzeit von HP und ]Xerox[ \nWie aus zwei wankenden Riesen eine Erfolgsstory]",

                "prefix": "\n<Title> Mögliche Elefantenhochzeit von HP und ",

                "exact": "Xerox",

                "suffix": " \nWie aus zwei wankenden Riesen eine Erfolgsstory",

                "offset": 58,

                "length": 5

            }

        ],

       

 

Relevance tag:

    	
            

 "relevance": 0.8,

      

 

topmostPublicParent resolution tag:

    	
            

  "resolutions": [

            {

                "name": "XEROX HOLDINGS CORPORATION",

                "permid": "5068338327",

                "primaryric": "XRX.N",

                "ispublic": "true",

                "commonname": "Xerox Hlngs",

                "topmostPublicParent": true,

                "id": "https://permid.org/1-5068338327"

            },

          

 

Company resolution tag:

    	
            

  {

                "name": "XEROX CORPORATION",

                "permid": "4295905360",

                "primaryric": "XRX.N",

                "ispublic": "false",

                "commonname": "Xerox",

                "score": 1,

                "id": "https://permid.org/1-4295905360",

                "ticker": "XRX"

            }

        ]

    }

 

Example 2: Company Extraction in JSON Output Format (Alphabet Inc)

Note that the related tags (instance, relevance, resolution, and confidence) are nested inside the Company entity tag.

    	
            

"http://d.opencalais.com/comphash-1/46a8dd15-7837-387f-b5e9-bd063d51be7c": {

        "_typeGroup": "entities",

        "_type": "Company",

        "forenduserdisplay": "true",

        "name": "Alphabet Inc",

        "confidencelevel": "0.868",

        "csepermid": "5030853586",

        "_typeReference": "http://s.opencalais.com/1/type/em/e/Company",

        "instances": [

            {

                "detection": "[drei Mal so groß wie Xerox. \n \n11 \n \nHP, Apple, ]Google[: In diesen Garagen wurden Weltkonzerne gegründet]",

                "prefix": "drei Mal so groß wie Xerox. \n \n11 \n \nHP, Apple, ",

                "exact": "Google",

                "suffix": ": In diesen Garagen wurden Weltkonzerne gegründet",

                "offset": 1279,

                "length": 6

            }

        ],

        "relevance": 0.2,

        "resolutions": [

            {

                "name": "ALPHABET INC.",

                "permid": "5030853586",

                "primaryric": "GOOGL.OQ",

                "ispublic": "true",

                "commonname": "Alphabet",

                "score": 1,

                "id": "https://permid.org/1-5030853586",

                "ticker": "GOOGL"

            }

        ],

        "confidence": {

            "statisticalfeature": "0.668",

            "dblookup": "0.0",

            "resolution": "1.0",

            "aggregate": "0.868"

        }

    },

 

Example 3: Company Extraction in JSON Output Format (Apple Inc)

Note that the related tags (resolution, instance, relevance, and confidence) are nested inside the Company entity tag.

    	
            

 "http://d.opencalais.com/comphash-1/d412df50-1061-3276-a052-5bbd85244633": {

        "_typeGroup": "entities",

        "_type": "Company",

        "forenduserdisplay": "true",

        "name": "Apple Inc",

        "confidencelevel": "0.868",

        "csepermid": "4295905573",

        "resolutions": [

            {

                "name": "APPLE INC.",

                "permid": "4295905573",

                "primaryric": "AAPL.OQ",

                "ispublic": "true",

                "commonname": "Apple",

                "score": 1,

                "id": "https://permid.org/1-4295905573",

                "ticker": "AAPL"

            }

        ],

        "_typeReference": "http://s.opencalais.com/1/type/em/e/Company",

        "instances": [

            {

                "detection": "[als drei Mal so groß wie Xerox. \n \n11 \n \nHP, ]Apple[, Google: In diesen Garagen wurden Weltkonzerne]",

                "prefix": "als drei Mal so groß wie Xerox. \n \n11 \n \nHP, ",

                "exact": "Apple",

                "suffix": ", Google: In diesen Garagen wurden Weltkonzerne",

                "offset": 1272,

                "length": 5

            }

        ],

        "relevance": 0.2,

        "confidence": {

            "statisticalfeature": "0.668",

            "dblookup": "0.0",

            "resolution": "1.0",

            "aggregate": "0.868"

        }

 

Example 4: Industry Tag in JSON Output Format

    	
            

  "http://d.opencalais.com/dochash-1/2045809c-0f29-3914-bba3-02350140454d/Industry/1": {

        "_typeGroup": "industry",

        "forenduserdisplay": "false",

        "name": "Phones & Smart Phones",

        "rcscode": "B:1769",

        "trbccode": "5710602011",

        "permid": "4294951232",

        "relevance": 0.2

    },

 

Example 5: Company Extraction in RDF Output Format (including all related tags and the topmost tag)

Intelligent Tagging assigns a unique ID (a hash tag) to the extracted entity. In this example, the hash tag for the extracted entity, Xerox Corp, is  comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c.

In the RDF output format, the same hash tag is displayed by the "subject" attribute of all the instance tags that identify mentions of Xerox Corp, linking them to the extracted entity and to each other. Likewise, the "subject" attribute of the related Relevance and Resolution tags also display the same hash tag.

(This is not relevant to the JSON output which nests all related tags within the entity markup tag.) 

Tip: Since the Intelligent Tagging output does not group metadata types, we highly recommend using the CalaisModel Abstraction Layer to simplify parsing the Intelligent Tagging RDF output. The Abstraction Layer is relevant to the RDF/XML response format only. For further information, see the Abstraction Layer Developer Guide. Abstraction Layer libraries are available on the Downloads tab.

Company entity markup tag (em/e/Company):

    	
            

  <rdf:Description rdf:about="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/em/e/Company"/>

        <c:forenduserdisplay>true</c:forenduserdisplay>

        <c:name>Xerox Corp</c:name>

        <c:confidencelevel>0.868</c:confidencelevel>

        <c:csepermid>4295905360</c:csepermid>

    </rdf:Description>

InstanceInfo tag:

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/2045809c-0f29-3914-bba3-02350140454d/Instance/3">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/InstanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/2045809c-0f29-3914-bba3-02350140454d"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <!--Company: Xerox Corp; -->

        <c:detection>[

&lt;Title&gt; Mögliche Elefantenhochzeit von HP und ]Xerox[ 

Wie aus zwei wankenden Riesen eine Erfolgsstory]</c:detection>

        <c:prefix>

&lt;Title&gt; Mögliche Elefantenhochzeit von HP und </c:prefix>

        <c:exact>Xerox</c:exact>

        <c:suffix> 

Wie aus zwei wankenden Riesen eine Erfolgsstory</c:suffix>

        <c:offset>58</c:offset>

        <c:length>5</c:length>

    </rdf:Description>

Confidence tag:

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/conf/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/tag/Confidence"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/2045809c-0f29-3914-bba3-02350140454d"/>

        <!--Xerox Corp-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <c:statisticalfeature>0.668</c:statisticalfeature>

        <c:dblookup>0.0</c:dblookup>

        <c:resolution>1.0</c:resolution>

        <c:aggregate>0.868</c:aggregate>

    </rdf:Description>

RelevanceInfo tag:

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/2045809c-0f29-3914-bba3-02350140454d/Relevance/3">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/RelevanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/2045809c-0f29-3914-bba3-02350140454d"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <c:relevance>0.8</c:relevance>

        <c:relevancecont>0.64</c:relevancecont>

    </rdf:Description>

Company Resolution tag (er/Company):

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/er/company/ralg-oa/4295905360">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/er/Company"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/2045809c-0f29-3914-bba3-02350140454d"/>

        <c:name>XEROX CORPORATION</c:name>

        <c:permid>4295905360</c:permid>

        <c:primaryric>XRX.N</c:primaryric>

        <c:ispublic>false</c:ispublic>

        <c:commonname>Xerox</c:commonname>

        <c:score>1.0</c:score>

        <!--Xerox Corp-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <c:legacyid rdf:resource="http://d.opencalais.com/er/company/ralg-tr1r/59dbf5cc-ca27-3463-a820-7e1c9bca78e3"/>

        <c:openpermid rdf:resource="https://permid.org/1-4295905360"/>

        <c:ticker>XRX</c:ticker>

    </rdf:Description>

TopmostPublicParentCompany tag:

    	
            

  <rdf:Description rdf:about="http://d.opencalais.com/er/topmostpublicparentcompany/ralg-oa-tm/5068338327">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/er/TopmostPublicParentCompany"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/2045809c-0f29-3914-bba3-02350140454d"/>

        <c:name>XEROX HOLDINGS CORPORATION</c:name>

        <c:permid>5068338327</c:permid>

        <c:primaryric>XRX.N</c:primaryric>

        <c:ispublic>true</c:ispublic>

        <c:commonname>Xerox Hlngs</c:commonname>

        <!--Xerox Corp-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <c:legacyid rdf:resource="http://d.opencalais.com/er/company/ralg-tr1r/922d30f3-4d37-365e-9c84-a6c9a8974f8e"/>

        <c:openpermid rdf:resource="https://permid.org/1-5068338327"/>

    </rdf:Description>

 

Example 6: Industry Tag in RDF Output Format

    	
            

<rdf:Description rdf:about="http://d.opencalais.com/dochash-1/2045809c-0f29-3914-bba3-02350140454d/Industry/2">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/tag/Industry"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/2045809c-0f29-3914-bba3-02350140454d"/>

        <c:forenduserdisplay>false</c:forenduserdisplay>

        <c:name>Commercial Document Management</c:name>

        <c:rcscode>B:1780</c:rcscode>

        <c:trbccode>5710501011</c:trbccode>

        <c:permid>4294951221</c:permid>

        <c:relevance>0.800</c:relevance>

    </rdf:Description>

 

Tagging Japanese Language Documents

Intelligent Tagging supports extraction of the following companies from Japanese intput text:

  • Companies listed on the Tokyo Stock Exchange (TSE)
  • S&P 500 companies
  • A few dozen supported non-Japanese companies that issue debt (bonds) in Japanese Yen (JPY).

 

Notes:

  • Japanese language processing is tested on XML input.
  • Internal Intelligent Tagging users: Japanese language processing is supported by the allMetadata profile only.

 

Mandatory Request Headers

Tagging Output

 

Mandatory Request Headers

To tag Japanese language input, the API call must define these headers (Internal Intelligent Tagging requires different headers, as described below). Furthermore, we highly recommend using the x-calais-language header for best results.

Header Value
Content-Type text/xml
x-ag-access-token The value of this header is your license key. (For Intelligent Tagging On Premise, this header is not supported and not relevant.)
x-calais-selectiveTags

Determines which of the supported tagging processes are triggered.

Valid values (not case sensitive): company; industry.

You can pass multiple values.

For example: x-calais-selectiveTags:company,industry

 

Internal Intelligent Tagging (for internal customers who do not connect through API Gateway) requires the following headers:

Header Value
x-calais-profile allMetadata
Content-Type text/xml
x-calais-licenseID The value of this header is your license key.
x-calais-selectiveTags

Determines which of the supported tagging processes are triggered.

Valid values (not case sensitive): company; industry.

You can pass multiple values.

For example: x-calais-selectiveTags:company,industry

 

For detailed information about the mandatory headers and also about the optional input headers that can be used to customize the Intelligent Tagging workflow to your use case, see Input Headers.

 

 

Japanese Tagging Output

When using Intelligent Tagging to extract companies from Japanese input text, the following metadata tags may be found in the output:

Instance tag

Company entity markup tag

Relevance tag

Confidence Tag

Company resolution tag

TopmostPublicParentCompany resolution tag

Industry tag

 

For a conceptual overview of the Intelligent Tagging Semantic Metadata Tags, see How Does Intelligent Tagging Work?

 

Japanese Output Examples

Example 1: Company Extraction in JSON Output Format (Xerox Corporation)

Example 2: Company Extraction in JSON Output Format (SoftBank Group)

Example 3: Industry Tag in JSON Output Format

Example 4: Company Extraction in RDF Output Format

Example 5: Industry Tag in RDF Output Format

 

 

Example 1: Company Extraction in JSON Output Format (Xerox Corporation)

In this output example, we have separated out the individual tags to make them easier to look at. However, please note that in the JSON output, related tags (confidence, resolution, instance, and relevance) are nested inside the entity tag.

 

Company entity tag:

    	
            

 "http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c": {

        "_typeGroup": "entities",

        "_type": "Company",

        "forenduserdisplay": "true",

        "name": "XEROX CORPORATION",

        "confidencelevel": "0.941",

        "csepermid": "4295905360",

        "_typeReference": "http://s.opencalais.com/1/type/em/e/Company",

      

Confidence tag:

    	
            

 "confidence": {

            "statisticalfeature": "0.862",

            "dblookup": "0.0",

            "resolution": "1.0",

            "aggregate": "0.941"

Instance tags:

    	
            

 "instances": [

            {

                "detection": "[米]ゼロックス[、敵対的買収も視野 HPに通告 \nエレクトロニクス 北米 \n2019/11/22 3:05 \n]",

                "prefix": "米",

                "exact": "ゼロックス",

                "suffix": "、敵対的買収も視野 HPに通告 \nエレクトロニクス 北米 \n2019/11/22 3:05 \n",

                "offset": 1,

                "length": 5

            },

            {

                "detection": "[\nエレクトロニクス 北米 \n2019/11/22 3:05 \n 保存 共有 印刷その他 \n]ゼロックス[はHPに資産査定への協力を迫った=ロイター \n画像の拡大]",

                "prefix": "\nエレクトロニクス 北米 \n2019/11/22 3:05 \n 保存 共有 印刷その他 \n",

                "exact": "ゼロックス",

                "suffix": "はHPに資産査定への協力を迫った=ロイター \n画像の拡大",

                "offset": 67,

                "length": 5

            },

            {

                "detection": "[保存 共有 印刷その他 \nゼロックスはHPに資産査定への協力を迫った=ロイター \n画像の拡大 \n]ゼロックス[はHPに資産査定への協力を迫った=ロイター \n]",

                "prefix": "保存 共有 印刷その他 \nゼロックスはHPに資産査定への協力を迫った=ロイター \n画像の拡大 \n",

                "exact": "ゼロックス",

                "suffix": "はHPに資産査定への協力を迫った=ロイター \n",

                "offset": 102,

                "length": 5

            }

        ],

Relevance tag:

    	
              "relevance": 0.8,
        
        
    

Company resolution tag:

    	
            

 "resolutions": [

            {               

                "name": "XEROX CORPORATION",

                "permid": "4295905360",

                "primaryric": "XRX.N",

                "ispublic": "false",

                "commonname": "Xerox",

                "score": 1,

                "id": "https://permid.org/1-4295905360",

                "ticker": "XRX"

            },

       

TopmostPublicParentCompany resolution tag:

    	
            

 {

                "name": "XEROX HOLDINGS CORPORATION",

                "permid": "5068338327",

                "primaryric": "XRX.N",

                "ispublic": "true",

                "commonname": "Xerox Hlngs",

                "topmostPublicParent": true,

                "id": "https://permid.org/1-5068338327"

            }

 ]

 

Example 2: Company Extraction in JSON Output Format (SoftBank Group)


Note that the related tags (resolution, instance, relevance, and confidence) are nested inside the Company entity tag.

Company entity tag:

    	
            

 "http://d.opencalais.com/comphash-1/24d394cb-d237-3c4c-8eb4-a6efc4b78739": {

        "_typeGroup": "entities",

        "_type": "Company",

        "forenduserdisplay": "true",

        "name": "SoftBank Group Corp.",

        "confidencelevel": "0.926",

        "csepermid": "4295877094",

        "resolutions": [

            {

                "name": "SoftBank Group Corp.",

                "permid": "4295877094",

                "primaryric": "9984.T",

                "ispublic": "true",

                "commonname": "SoftBank Group",

                "score": 1,

                "id": "https://permid.org/1-4295877094",

                "ticker": "9984"

            }

        ],

        "_typeReference": "http://s.opencalais.com/1/type/em/e/Company",

        "instances": [

            {

                "detection": "[]ソフトバンク[155億円営業赤字 米ウィーで5千億円損失 \n \n2019年11月6日 22時46分 \n \n ]",

                "exact": "ソフトバンク",

                "suffix": "155億円営業赤字 米ウィーで5千億円損失 \n \n2019年11月6日 22時46分 \n \n ",

                "offset": 0,

                "length": 6

            },

            {

                "detection": "[\n \n2019年11月6日 22時46分 \n \n  \n ]ソフトバンクグループ[が6日発表した2019年9月中間連結決算は、本業のもうけを示す営業損益が155億円の赤字(前年同期は]",

                "prefix": "\n \n2019年11月6日 22時46分 \n \n  \n ",

                "exact": "ソフトバンクグループ",

                "suffix": "が6日発表した2019年9月中間連結決算は、本業のもうけを示す営業損益が155億円の赤字(前年同期は",

                "offset": 56,

                "length": 10

            },

            {

                "detection": "[事業で約5700億円の損失を計上した。海外の先端企業に巨額を投じる戦略に陰りが見え始めた。 \n \n ]ソフトバンク[は経営難に陥るウィーに対して総額約1兆円の支援を実施する計画で、再建が難航すれば損失がさらに拡大する]",

                "prefix": "事業で約5700億円の損失を計上した。海外の先端企業に巨額を投じる戦略に陰りが見え始めた。 \n \n ",

                "exact": "ソフトバンク",

                "suffix": "は経営難に陥るウィーに対して総額約1兆円の支援を実施する計画で、再建が難航すれば損失がさらに拡大する",

                "offset": 251,

                "length": 6

            }

        ],

        "relevance": 0.8,

        "confidence": {

            "statisticalfeature": "0.785",

            "dblookup": "0.0",

            "resolution": "1.0",

            "aggregate": "0.926"

        }

    }

}

 

Example 3: Industry Tag in JSON Output Format

    	
            

 "http://d.opencalais.com/dochash-1/200e7689-8137-38c6-8ef2-ab605255d619/Industry/1": {

        "_typeGroup": "industry",

        "forenduserdisplay": "false",

        "name": "Software - NEC",

        "rcscode": "B:1796",

        "trbccode": "5720102010",

        "permid": "4294951205",

        "relevance": 0.8

 

Example 4: Company Extraction in RDF Output Format

Intelligent Tagging assigns a unique ID (a hash tag) to the extracted entity. In this example, the hash tag for the extracted entity, Xerox Corporation, is  comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c.

In the RDF output format, the same hash tag is displayed by the "subject" attribute of all the instance tags that identify mentions of Xerox Corporation, linking them to the extracted entity and to each other. Likewise, the "subject" attribute of the related Relevance and Resolution tags also display the same hash tag.

(This is not relevant to the JSON output which nests all related tags within the entity markup tag.) 

Tip: Since the Intelligent Tagging output does not group metadata types, we highly recommend using the CalaisModel Abstraction Layer to simplify parsing the Intelligent Tagging RDF output.  The Abstraction Layer is relevant to the RDF/XML response format only. For further information, see the Abstraction Layer Developer Guide. Abstraction Layer libraries are available on the Downloads tab.

 

Company entity markup tag (em/e/Company):

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/em/e/Company"/>

        <c:forenduserdisplay>true</c:forenduserdisplay>

        <c:name>XEROX CORPORATION</c:name>

        <c:confidencelevel>0.941</c:confidencelevel>

        <c:csepermid>4295905360</c:csepermid>

    </rdf:Description>

Confidence tag:

    	
            

<rdf:Description rdf:about="http://d.opencalais.com/conf/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/tag/Confidence"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/693941ce-fbcb-37fd-b717-a93e57b9947b"/>

        <!--XEROX CORPORATION-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <c:statisticalfeature>0.862</c:statisticalfeature>

        <c:dblookup>0.0</c:dblookup>

        <c:resolution>1.0</c:resolution>

        <c:aggregate>0.941</c:aggregate>

    </rdf:Description>

InstanceInfo tags:

    	
            

<rdf:Description rdf:about="http://d.opencalais.com/dochash-1/693941ce-fbcb-37fd-b717-a93e57b9947b/Instance/1">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/InstanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/693941ce-fbcb-37fd-b717-a93e57b9947b"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <!--Company: XEROX CORPORATION; -->

        <c:detection>[米]ゼロックス[、敵対的買収も視野 HPに通告 

エレクトロニクス 北米 

2019/11/22 3:05 

]</c:detection>

        <c:prefix>米</c:prefix>

        <c:exact>ゼロックス</c:exact>

        <c:suffix>、敵対的買収も視野 HPに通告 

エレクトロニクス 北米 

2019/11/22 3:05 

</c:suffix>

        <c:offset>1</c:offset>

        <c:length>5</c:length>

    </rdf:Description>

    	
            

  <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/693941ce-fbcb-37fd-b717-a93e57b9947b/Instance/2">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/InstanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/693941ce-fbcb-37fd-b717-a93e57b9947b"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <!--Company: XEROX CORPORATION; -->

        <c:detection>[

エレクトロニクス 北米 

2019/11/22 3:05 

 保存 共有 印刷その他 

]ゼロックス[はHPに資産査定への協力を迫った=ロイター 

画像の拡大]</c:detection>

        <c:prefix>

エレクトロニクス 北米 

2019/11/22 3:05 

 保存 共有 印刷その他 

</c:prefix>

        <c:exact>ゼロックス</c:exact>

        <c:suffix>はHPに資産査定への協力を迫った=ロイター 

画像の拡大</c:suffix>

        <c:offset>67</c:offset>

        <c:length>5</c:length>

    </rdf:Description>

RelevanceInfo tag:

    	
            

  <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/693941ce-fbcb-37fd-b717-a93e57b9947b/Relevance/1">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/RelevanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/693941ce-fbcb-37fd-b717-a93e57b9947b"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <c:relevance>0.8</c:relevance>

        <c:relevancecont>0.92</c:relevancecont>

    </rdf:Description>

Company Resolution Tag (er/Company):

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/er/company/ralg-oa/4295905360">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/er/Company"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/693941ce-fbcb-37fd-b717-a93e57b9947b"/>

        <c:name>XEROX CORPORATION</c:name>

        <c:permid>4295905360</c:permid>

        <c:primaryric>XRX.N</c:primaryric>

        <c:ispublic>false</c:ispublic>

        <c:commonname>Xerox</c:commonname>

        <c:score>1.0</c:score>

        <!--XEROX CORPORATION-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <c:legacyid rdf:resource="http://d.opencalais.com/er/company/ralg-tr1r/59dbf5cc-ca27-3463-a820-7e1c9bca78e3"/>

        <c:openpermid rdf:resource="https://permid.org/1-4295905360"/>

        <c:ticker>XRX</c:ticker>

    </rdf:Description>

TopmostPublicParentCompany Resolution Tag (er/topmostPublicParentCompany):

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/er/topmostpublicparentcompany/ralg-oa-tm/5068338327">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/er/TopmostPublicParentCompany"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/693941ce-fbcb-37fd-b717-a93e57b9947b"/>

        <c:name>XEROX HOLDINGS CORPORATION</c:name>

        <c:permid>5068338327</c:permid>

        <c:primaryric>XRX.N</c:primaryric>

        <c:ispublic>true</c:ispublic>

        <c:commonname>Xerox Hlngs</c:commonname>

        <!--XEROX CORPORATION-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/db183aec-3ef7-3a6c-b87c-a4ec938bf37c"/>

        <c:legacyid rdf:resource="http://d.opencalais.com/er/company/ralg-tr1r/922d30f3-4d37-365e-9c84-a6c9a8974f8e"/>

        <c:openpermid rdf:resource="https://permid.org/1-5068338327"/>

    </rdf:Description>

 

Example 5: Industry Tag in RDF Output Format

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/200e7689-8137-38c6-8ef2-ab605255d619/Industry/1">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/tag/Industry"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/200e7689-8137-38c6-8ef2-ab605255d619"/>

        <c:forenduserdisplay>false</c:forenduserdisplay>

        <c:name>Software - NEC</c:name>

        <c:rcscode>B:1796</c:rcscode>

        <c:trbccode>5720102010</c:trbccode>

        <c:permid>4294951205</c:permid>

        <c:relevance>0.800</c:relevance>

    </rdf:Description>

 

Supported JPY Bond Issuers

In addition to TSE and S&P 500 companies, Intelligent Tagging also extracts from Japanese text the following non-Japanese companies that issue debt (bonds) in Japanese Yen. 

RIC

Company Name (English)

Company Name (Japanese)

005490.KS

POSCO

株式会社 ポスコ

024110.KS

Industrial Bank Of Korea

中小企業銀行

036460.KS

Korea Gas Corporation

韓国ガス公社

8404.T^H11

Mizuho Trust & Banking Co.,Ltd.

みずほアセット信託銀行

ABZRF.PK^C06

ABN AMRO Bank NV

ABNアムロ銀行

ANZ.AX

Australia and New Zealand Banking Group Limited

オーストラリア・ニュージーランド銀行

BACA.VI^E08

Unicredit Bank Austria AG

オーストリア銀行

BNPP.PA

BNP PARIBAS SA

BNPパリバ SA

BSC.N^F08

The Bear Stearns Companies LLC

ザ・ベアー・スターンズ・カンパニーズ LLC

BSCT.L^I01

Bank of Scotland plc

ロイヤル・バンク・オブ・スコットランド

CATO.N

Dexia Credit Local SA

デクシア・クレディ・ローカル銀行

CBA.AX

Commonwealth Bank of Australia

オーストラリア・コモンウェルス銀行

CCL.AX

Coca-Cola Amatil Limited

コカコーラ・アマテール

CH064868=BEKB

Korea Container Terminal Authority

韓国コンテナ埠頭公団

DAIGn.DE

Daimler AG

ダイムラークライスラー AG

DTEGn.DE

Deutsche Telekom AG

ドイツテレコム AG

EDC.PS

PNOC Energy Development Corporation

フィリピン国営石油会社

EYJ^D09

Citigroup Funding Inc.

シティグループ・ファンディング

FR0000415929.LUF

LS Corp.

LS電線

HCU.L^H07

Hitachi Capital (UK) PLC

ヒタチキャピタル(UK) PLC

IFK^L10

International Finance Corporation

国際金融公社

LEHMQ.PK^C12

Lehman Brothers Holdings Inc.

リーマン・ブラザーズ・ホールディングズ・インク

MERDJ.PK^B11

Merrill Lynch & Co., Inc.

メリルリンチ・アンド・カンパニー・インク

NAB.AX

National Australia Bank Limited

ナショナル・オーストラリア・バンク・リミテッド

ORAN.PA

France Telecom SA

フランス・テレコム SA

PTT.BK

PTT Public Company Limited

タイ石油公社

RENA.PA

Renault SA

ルノー SA

RJZ.P

AB Svensk Exportkredit

スウェーデン輸出信用銀行

RY.TO

Royal Bank of Canada

ロイヤル・バンク・オブ・カナダ

TSCO.L

Tesco PLC

テスコ・ピー・エル・シー

UBSN.S^H15

UBS AG

ユービーエス・エイ・ジー

UDNT.K^B15

Deutsche Bank AG

ドイツ銀行 AG

WATH.L^G93

Thames Water Limited

テームズ・ウォーター

 

 

 

 

Tagging Spanish Language Documents

Intelligent Tagging can extract the following entities from Spanish input text: City, Company, Continent, Country, EmailAddress, FaxNumber, MarketIndex, NaturalFeature, Organization, Person, PhoneNumber, ProvinceOrState, Region, URL.

 

The tagging output may include any of these tags:

 

For a conceptual overview of the Intelligent Tagging Semantic Metadata Tags, see How Does Intelligent Tagging Work?

 

Mandatory Request Headers

Output Examples

 

Mandatory Request Headers

To tag Spanish language input, the API call must define these headers (Internal Intelligent Tagging requires different headers, as described below). Furthermore, we highly recommend using the x-calais-language header for best results.

Header Value
Content-Type Valid values: text/xml; text/raw; text/html; application/pdf.
x-ag-access-token The value of this header is your license key. (For Intelligent Tagging On Premise, this header is not supported and not relevant.)
x-calais-selectiveTags

Determines which of the supported tagging processes are triggered.

Valid values: city; company; continent; country; emailAddress; faxNumber; marketIndex; naturalFeature; organization; person; phoneNumber; provinceOrState; region; url; industry.

Values are not case sensitive.

You can pass multiple values.

For example, the following triggers city, company, and person tagging: x-calais-selectiveTags:city,company,person

 

Internal Intelligent Tagging (for internal customers who do not connect through API Gateway) requires the following headers:

Header Value
x-calais-profile allMetadata
Content-Type Valid values: text/xml; text/raw; text/html; application/pdf.
x-calais-licenseID The value of this header is your license key.
x-calais-selectiveTags

Determines which of the supported tagging processes are triggered.

Valid values: city; company; continent; country; emailAddress; faxNumber; marketIndex; naturalFeature; organization; person; phoneNumber; provinceOrState; region; url; industry.

Values are not case sensitive.

You can pass multiple values.

For example, the following triggers city, company, and person tagging: x-calais-selectiveTags:city,company,person

 

For detailed information about the mandatory headers and also about the optional input headers that can be used to customize the Intelligent Tagging workflow to your use case, see Input Headers.

 

 

Spanish Output Examples

Example 1: Company Extraction in JSON Output Format

Example 2: Person Extraction in JSON Output Format

Example 3: Country Extraction in JSON Output Format

Example 4: Industry Tag in JSON Output Format

Example 5: Company Extraction in RDF Output Format

Example 6: Person Extraction in RDF Output Format

Example 7: Industry Tag in RDF Output Format

Additional Examples for all supported entity types


 

Example 1: Company Extraction in JSON Output Format

In this output example, we have separated out the individual tags to make them easier to look at. However, please note that in the JSON output, related tags (such as Instance, Relevance, Confidence, and Resolution) are nested inside the entity tag.

 

Company entity tag:

    	
            

 "http://d.opencalais.com/comphash-1/c7172a98-4c8a-31a9-bfd4-ce426c8db3c0": {

        "_typeGroup": "entities",

        "_type": "Company",

        "forenduserdisplay": "true",

        "name": "Google",

        "confidencelevel": "0.825",

        "recognizedas": "name",

        "_typeReference": "http://s.opencalais.com/1/type/em/e/Company",

       

Instance tag:

    	
            

 "instances": [

            {

                "detection": "[\nMucho tiempo antes de Microsoft, Apple o ]Google[ es establecieran como grandes marcas de]",

                "prefix": "\nMucho tiempo antes de Microsoft, Apple o ",

                "exact": "Google",

                "suffix": " es establecieran como grandes marcas de",

                "offset": 4778,

                "length": 6

            }

        ],

Relevance tag:

    	
             "relevance": 0.2,
        
        
    

Confidence tag:

    	
            

 "confidence": {

            "statisticalfeature": "0.644",

            "dblookup": "0.0",

            "resolution": "1.0",

            "aggregate": "0.825"

        },

       

Company Resolution tag:

    	
            

 "resolutions": [

            {

                "name": "GOOGLE LLC",

                "permid": "4295899948",

                "ispublic": "false",

                "commonname": "Google",

                "score": 1,

                "id": "https://permid.org/1-4295899948"

            },

          

TopmostPublicParent resolution tag:

    	
            

  {

                "name": "ALPHABET INC.",

                "permid": "5030853586",

                "primaryric": "GOOGL.OQ",

                "ispublic": "true",

                "commonname": "Alphabet",

                "topmostPublicParent": true,

                "id": "https://permid.org/1-5030853586",

                "ticker": "GOOGL"

            }

 

Example 2: Person Extraction in JSON Output Format

Note that the related tags (instance, relevance, confidence) are nested inside the Person entity tag.

    	
            

 "http://d.opencalais.com/pershash-1/b01df276-57d6-3ee1-a85b-d28ecc99d182": {

        "_typeGroup": "entities",

        "_type": "Person",

        "forenduserdisplay": "true",

        "name": "Carlos Slim",

        "commonname": "Carlos Slim",

        "_typeReference": "http://s.opencalais.com/1/type/em/e/Person",

        "permid": "https://permid.org/1-404011",

        "instances": [

            {

                "detection": "[aumentó 40%. En 2014, el empresario mexicano ]Carlos Slim[ planteó una semana laboral de tres días. ¿Es]",

                "prefix": "aumentó 40%. En 2014, el empresario mexicano ",

                "exact": "Carlos Slim",

                "suffix": " planteó una semana laboral de tres días. ¿Es",

                "offset": 319,

                "length": 11

            }

        ],

        "relevance": 0.2,

        "confidence": {

            "statisticalfeature": "0.507",

            "dblookup": "0.95",

            "resolution": "0.0",

            "aggregate": "0.585"

 

Example 3: Country Extraction in JSON Output Format

Again, note that the related tags (instance, relevance) are nested inside the entity tag.

    	
            

 "http://d.opencalais.com/genericHasher-1/3daf3e62-7799-3e96-94ab-73194702284e": {

        "_typeGroup": "entities",

        "_type": "Country",

        "forenduserdisplay": "true",

        "name": "Japón",

        "_typeReference": "http://s.opencalais.com/1/type/em/e/Country",

        "instances": [

            {

                "detection": "[de una prueba reciente en Microsoft en ]Japón[ sugieren que podría funcionar incluso para las]",

                "prefix": "de una prueba reciente en Microsoft en ",

                "exact": "Japón",

                "suffix": " sugieren que podría funcionar incluso para las",

                "offset": 182,

                "length": 5

            }

        ],

        "relevance": 0.2

 

Example 4: Industry Tag in JSON Output Format

    	
            

 "http://d.opencalais.com/dochash-1/55483b3d-acb3-3e10-8210-ef61e402cf93/Industry/1": {

        "_typeGroup": "industry",

        "forenduserdisplay": "false",

        "name": "Software - NEC",

        "rcscode": "B:1796",

        "trbccode": "5720102010",

        "permid": "4294951205",

        "relevance": 0.2

 

Example 5: Company Extraction in RDF Output Format

Intelligent Tagging assigns a unique ID (a hash tag) to the extracted entity. In this example, the hash tag for the extracted entity, Google, is  comphash-1/c7172a98-4c8a-31a9-bfd4-ce426c8db3c0.

In the RDF output format, the same hash tag is displayed by the "subject" attribute of all the instance tags that identify mentions ofGoogle, linking them to the extracted entity and to each other. Likewise, the "subject" attribute of the related Confidence, Relevance, and Resolution tags also display the same hash tag.

(This is not relevant to the JSON output which nests all related tags within the entity markup tag.) 

Tip: Since the Intelligent Tagging output does not group metadata types, we highly recommend using the CalaisModel Abstraction Layer to simplify parsing the Intelligent Tagging RDF output. The Abstraction Layer is relevant to the RDF/XML response format only. The Abstraction Layer is relevant to the RDF/XML response format only. For further information, see the Abstraction Layer Developer Guide. Abstraction Layer libraries are available on the Downloads tab.

Company entity markup tag (em/e/Company):

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/comphash-1/c7172a98-4c8a-31a9-bfd4-ce426c8db3c0">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/em/e/Company"/>

        <c:forenduserdisplay>true</c:forenduserdisplay>

        <c:name>Google</c:name>

        <c:confidencelevel>0.825</c:confidencelevel>

        <c:recognizedas>name</c:recognizedas>

    </rdf:Description>

Confidence tag:

    	
            

<rdf:Description rdf:about="http://d.opencalais.com/conf/comphash-1/c7172a98-4c8a-31a9-bfd4-ce426c8db3c0">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/tag/Confidence"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/9a39dc66-4673-3a8c-b3d1-d77c328e5a09"/>

        <!--Google-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/c7172a98-4c8a-31a9-bfd4-ce426c8db3c0"/>

        <c:statisticalfeature>0.644</c:statisticalfeature>

        <c:dblookup>0.0</c:dblookup>

        <c:resolution>1.0</c:resolution>

        <c:aggregate>0.825</c:aggregate>

    </rdf:Description>

RelevanceInfo tag:

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/9a39dc66-4673-3a8c-b3d1-d77c328e5a09/Relevance/6">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/RelevanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/9a39dc66-4673-3a8c-b3d1-d77c328e5a09"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/c7172a98-4c8a-31a9-bfd4-ce426c8db3c0"/>

        <c:relevance>0.2</c:relevance>

        <c:relevancecont>0.03</c:relevancecont>

    </rdf:Description>

InstanceInfo tag:

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/dochash-1/9a39dc66-4673-3a8c-b3d1-d77c328e5a09/Instance/32">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/sys/InstanceInfo"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/9a39dc66-4673-3a8c-b3d1-d77c328e5a09"/>

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/c7172a98-4c8a-31a9-bfd4-ce426c8db3c0"/>

        <!--Company: Google; -->

        <c:detection>[

Mucho tiempo antes de Microsoft, Apple o ]Google[ es establecieran como grandes marcas de]</c:detection>

        <c:prefix>

Mucho tiempo antes de Microsoft, Apple o </c:prefix>

        <c:exact>Google</c:exact>

        <c:suffix> es establecieran como grandes marcas de</c:suffix>

        <c:offset>4778</c:offset>

        <c:length>6</c:length>

    </rdf:Description>

Company Resolution Tag (er/Company):

    	
            

<rdf:Description rdf:about="http://d.opencalais.com/er/company/ralg-oa/4295899948">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/er/Company"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/9a39dc66-4673-3a8c-b3d1-d77c328e5a09"/>

        <c:name>GOOGLE LLC</c:name>

        <c:permid>4295899948</c:permid>

        <c:ispublic>false</c:ispublic>

        <c:commonname>Google</c:commonname>

        <c:score>1.0</c:score>

        <!--Google-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/c7172a98-4c8a-31a9-bfd4-ce426c8db3c0"/>

        <c:legacyid rdf:resource="http://d.opencalais.com/er/company/ralg-tr1r/ce181d44-1915-3387-83da-0dc4ec01c6da"/>

        <c:openpermid rdf:resource="https://permid.org/1-4295899948"/>

    </rdf:Description>

TopmostPublicParentCompany tag:

    	
            

<rdf:Description rdf:about="http://d.opencalais.com/er/topmostpublicparentcompany/ralg-oa-tm/5030853586">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/er/TopmostPublicParentCompany"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/9a39dc66-4673-3a8c-b3d1-d77c328e5a09"/>

        <c:name>ALPHABET INC.</c:name>

        <c:permid>5030853586</c:permid>

        <c:primaryric>GOOGL.OQ</c:primaryric>

        <c:ispublic>true</c:ispublic>

        <c:commonname>Alphabet</c:commonname>

        <!--Google-->

        <c:subject rdf:resource="http://d.opencalais.com/comphash-1/c7172a98-4c8a-31a9-bfd4-ce426c8db3c0"/>

        <c:legacyid rdf:resource="http://d.opencalais.com/er/company/ralg-tr1r/8419f2c5-01d5-3945-a0d8-b6e8c1b685dd"/>

        <c:openpermid rdf:resource="https://permid.org/1-5030853586"/>

        <c:ticker>GOOGL</c:ticker>

    </rdf:Description>

 

Example 6: Person Entity Tag in RDF Output Format

 

    	
            

 <rdf:Description rdf:about="http://d.opencalais.com/pershash-1/b01df276-57d6-3ee1-a85b-d28ecc99d182">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/em/e/Person"/>

        <c:permid rdf:resource="https://permid.org/1-404011"/>

        <c:forenduserdisplay>true</c:forenduserdisplay>

        <c:name>Carlos Slim</c:name>

        <c:commonname>Carlos Slim</c:commonname>

    </rdf:Description>

 

Example 7: Industry Tag in RDF Output Format

    	
            

<rdf:Description rdf:about="http://d.opencalais.com/dochash-1/55483b3d-acb3-3e10-8210-ef61e402cf93/Industry/1">

        <rdf:type rdf:resource="http://s.opencalais.com/1/type/tag/Industry"/>

        <c:docId rdf:resource="http://d.opencalais.com/dochash-1/55483b3d-acb3-3e10-8210-ef61e402cf93"/>

        <c:forenduserdisplay>false</c:forenduserdisplay>

        <c:name>Software - NEC</c:name>

        <c:rcscode>B:1796</c:rcscode>

        <c:trbccode>5720102010</c:trbccode>

        <c:permid>4294951205</c:permid>

        <c:relevance>0.200</c:relevance>

    </rdf:Description>

 

Additional Examples for all Supported Entity Types

City Entity

Input Text: La alta costura de París piensa en grande.

Extracted Entity: Paris

 

Input Text: los ataques del 11 de septiembre en Nueva York.

Extracted Entity: Neuva York

 

Company Entity

Input Text: Las cenizas de los Roddenberry serán lanzadas al espacio dentro de un año y medio, según sus deseos, dijo el lunes la compañía de vuelos espaciales Celestis Inc. Majel Barrett Roddenberry.

Extracted Entity: Celestis Inc.

 

Input Text: Iberdrola subió un 1,5 por ciento a 6,10 euros.

Extracted Entity: Iberdrola

 

Continent Entity

Input Text: La Antártida se está calentando, no enfriando, según un estudio

Extracted Entity: Antártida

Input Text: Jackson ha pospuesto seis apariciones en Norteamérica.
Extracted Entity: Norteamérica

 

Country Entity

Input Text: La policía de Bahamas investiga la acusación de extorsión a Travolta.

Extracted Entity: Bahamas

 

Input Text: L'ancien Beatles, Paul McCartney, est parti en voyage aux États-Unis avec sa compagne, Nancy Shevell.

Extracted Entity: États-Unis

 

EmailAddress Entity

Input Text:

La Escuela Comunitaria de Austin 
P.O. Box 6176 
Austin, TX 78762-6176 
oficina: 512-554-8930 
fax: 866-868-9973 
info@austincommunityschool.org 

Extracted Entity: info@austincommunityschool.org 

 

FaxNumber  Entity

Input Text: 

Tamarindo - Ciudad 
100 Metros Norte y 25 Metros Oeste de Nunciatura 
Tel. +(506) 2653-0012 Fax: +(506) 2653-0012

Extracted Entity: +(506) 2653-0012

 

MarketIndex Entity

Input Text: A las 0844 GMT el îndice paneuropeo FTSEurofirst 300 subîa un 0,3 por ciento.

Extracted Entity:  FTSEurofirst 300

 

NaturalFeature Entity

Input Text: Por primera vez en España, organizamos una expedición al corazón del Himalaya hindú, al macizo de Garhwal.

Extracted Entity: Himilaya

Extracted Entity: Garhwal

 

Input Text: Caminar sobre el milenario hielo azul, navegar por el lago Argentino para ver de cerca otros gigantescos ríos de hielo.

Extracted Entity: lago Argentino

 

Organization Entity

Input Text: Vegara dijo que la tasa de paro no llegaría al 18,7 por ciento pronosticado por la Comisión Europea en 2010

Extracted Entity: la Comisión Europea

 

Input Text: cada vez que la Casa Blanca pasa de un partido a otro.

Extracted Entity: Casa Blanca

 

Person Entity

Input Text: Las autoridades de Bahamas están investigando un supuesto intento de extorsión contra el actor John Travolta.

Extracted Entity: John Travolta

 

Input Text: El comisario de policía Reginald Ferguson dijo que sus agentes estaban investigando una queja de extorsión.

Extracted Entity: Reginald Ferguson

 

PhoneNumber Entity

Input Text: 

La Escuela Comunitaria de Austin 
P.O. Box 6176 
Austin, TX 78762-6176 
oficina: 512-554-8930 fax: 866-868-9973 
info@austincommunityschool.org
http://www.campo21.com/

Extracted Entity: 512-554-8930

 

 

ProvinceOrState Entity

Input Text: Interior confirmó la detención de otras tres personas en Vizcaya, dos en Guipúzcoa y una en Álava.

Extracted Entity: Vizcaya

Extracted Entity: Guipúzcoa

Extracted Entity: Álava

 

Input Text: por la noche en la provincia de La Coruña.

Extracted Entity: La Coruña

 

 

Region Entity

Input Text: Dos personas han muerto en España debido al temporal de viento que afecta al norte de la Península Ibérica.
Extracted Entity: Península Ibérica

 

Input Text: en el suroeste de Francia se registraron fuertes vientos.
Extracted Entity: el suroeste de Francia

 

 

URL Entity

Input Text: Visite el nuevo portal http://www.carilo.com.ar toda la información de esta ciudad, servicios, alojamientos, inmobiliarias, etc.
Extracted Entity: http://www.carilo.com.ar