Named entity recognition tools: Difference between revisions

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Named entity recognition (NER) 或稱命名實體辨識、專有名詞辨識
Named entity recognition (NER) 或稱[https://zh.wikipedia.org/wiki/%E5%91%BD%E5%90%8D%E5%AE%9E%E4%BD%93%E8%AF%86%E5%88%AB 命名實體識別]、專有名詞識別


== CKIP Neural Chinese Word Segmentation, POS Tagging, and NER ==
== CKIP Neural Chinese Word Segmentation, POS Tagging, and NER ==
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<table border="1" class="wikitable sortable">
<table border="1" class="wikitable sortable">
<tr><th>Class name in English</th><th>Class name in Chinese</th></tr>
<tr><th>Class name in English</th><th>Class name in Traditional Chinese</th></tr>
<tr><td>person</td><td>人名</td></tr>
<tr><td>person</td><td>人名</td></tr>
<tr><td>norp</td><td>團體</td></tr>
<tr><td>norp</td><td>團體</td></tr>
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* language support:
* language support:
* classes of entity: "For English, by default, this annotator recognizes named (PERSON, LOCATION, ORGANIZATION, MISC), numerical (MONEY, NUMBER, ORDINAL, PERCENT), and temporal (DATE, TIME, DURATION, SET) entities (12 classes). <ref>[https://stanfordnlp.github.io/CoreNLP/ner.html#description Named Entity Recognition – NERClassifierCombiner | Stanford CoreNLP]</ref>"
* classes of entity: "For English, by default, this annotator recognizes named (PERSON, LOCATION, ORGANIZATION, MISC), numerical (MONEY, NUMBER, ORDINAL, PERCENT), and temporal (DATE, TIME, DURATION, SET) entities (12 classes). <ref>[https://stanfordnlp.github.io/CoreNLP/ner.html#description Named Entity Recognition – NERClassifierCombiner | Stanford CoreNLP]</ref>"
== spaCy ==
[https://spacy.io/ spaCy · Industrial-strength Natural Language Processing in Python]
* license: [https://github.com/explosion/spaCy/blob/master/LICENSE MIT License] {{Gd}}
* language support:
* classes of entity: "PERSON, NORP, FAC, ORG, GPE, LOC, PRODUCT, EVENT, WORK_OF_ART, LAW, LANGUAGE, DATE, TIME, PERCENT, MONEY, QUANTITY, ORDINAL and CARDINAL <ref>[https://spacy.io/api/annotation#named-entities Annotation Specifications · spaCy API Documentation]</ref>"


== Google Cloud Natural Language ==
== Google Cloud Natural Language ==
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* classes of entity: "Date, Duration, EmailAddress, Facility, GeographicFeature, Hashtag, IPAddress, JobTitle, Location and more ..."<ref>[https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-entity-types-version-2&locale=en Entity types (Version 2)]</ref>
* classes of entity: "Date, Duration, EmailAddress, Facility, GeographicFeature, Hashtag, IPAddress, JobTitle, Location and more ..."<ref>[https://cloud.ibm.com/docs/services/natural-language-understanding?topic=natural-language-understanding-entity-types-version-2&locale=en Entity types (Version 2)]</ref>


== spaCy ==
== 卓騰語言科技中文斷詞 ==
[https://spacy.io/ spaCy · Industrial-strength Natural Language Processing in Python]
[https://api.droidtown.co/ 卓騰語言科技中文斷詞 API]
* license:
* language support: Traditional Chinese
* classes of entity: "person, location, time, measurement and more ... <ref>[https://api.droidtown.co/document/ 卓騰語言科技中文斷詞 API]</ref>"
 
== BosonNLP ==
[https://bosonnlp.com/ BosonNLP]
* license:  
* license:  
* language support:
* language support: simplified Chinese
* classes of entity: "PERSON, NORP, FAC, ORG, GPE, LOC, PRODUCT, EVENT, WORK_OF_ART, LAW, LANGUAGE, DATE, TIME, PERCENT, MONEY, QUANTITY, ORDINAL and CARDINAL <ref>[https://spacy.io/api/annotation#named-entities Annotation Specifications · spaCy API Documentation]</ref>"
* classes of entity: "time, location, person_name, org_name, company_name, product_name and job_title <ref>[http://docs.bosonnlp.com/ner.html 命名实体识别 — BosonNLP HTTP API 1.0 documentation]</ref>"
 
<table border="1" class="wikitable sortable">
<tr><th>Class name in English</th><th>Class name in Simplified Chinese</th><th>Class name in Traditional Chinese</th></tr>
<tr><td>time</td><td>时间</td><td>時間</td></tr>
<tr><td>location</td><td>地点</td><td>地點</td></tr>
<tr><td>person_name</td><td>人名</td><td>人名</td></tr>
<tr><td>org_name</td><td>组织名</td><td>組織名</td></tr>
<tr><td>company_name</td><td>公司名</td><td>公司名</td></tr>
<tr><td>product_name</td><td>产品名</td><td>產品名</td></tr>
<tr><td>job_title</td><td>职位</td><td>職位</td></tr>
</table>


== References ==
== References ==

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