Projecting named entity tags from a resource rich language to a resource poor language
Named Entities (NE) are the prominent entities appearing in textual documents.Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. Named Entity Recognition (NER) is the identification of words in text that correspond to a...
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia Press
2012
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| Online Access: | https://repo.uum.edu.my/id/eprint/24088/1/J%20ICT%2012%202013%20121%E2%80%93146.pdf |
| _version_ | 1803628659073875968 |
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| author | Zamin, Norshuhani Oxley, Alan Abu Bakar, Zainab |
| author_facet | Zamin, Norshuhani Oxley, Alan Abu Bakar, Zainab |
| author_sort | Zamin, Norshuhani |
| collection | UUM |
| description | Named Entities (NE) are the prominent entities appearing in textual documents.Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. Named Entity Recognition (NER) is the identification of words in text that correspond to a pre-defined taxonomy such as person, organization, location, date, time, etc.This article focuses on the person (PER), organization (ORG) and location (LOC) entities for a Malay journalistic corpus of terrorism.A projection algorithm, using the Dice Coefficient function and bigram scoring method with domain-specific rules, is suggested to map the NE information from the English corpus to the Malay corpus of terrorism.The English corpus is the translated version of the Malay corpus.Hence, these two corpora are treated as parallel corpora. The method computes the string similarity between the English words and the list of available lexemes in a pre-built lexicon that approximates the best NE mapping.The algorithm has been effectively evaluated using our own terrorism tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure.An evaluation of the selected open source NER tool for English is also presented. |
| first_indexed | 2024-07-04T06:25:28Z |
| format | Article |
| id | uum-24088 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:25:28Z |
| publishDate | 2012 |
| publisher | Universiti Utara Malaysia Press |
| record_format | dspace |
| spelling | uum-240882018-05-06T23:42:45Z https://repo.uum.edu.my/id/eprint/24088/ Projecting named entity tags from a resource rich language to a resource poor language Zamin, Norshuhani Oxley, Alan Abu Bakar, Zainab QA75 Electronic computers. Computer science Named Entities (NE) are the prominent entities appearing in textual documents.Automatic classification of NE in a textual corpus is a vital process in Information Extraction and Information Retrieval research. Named Entity Recognition (NER) is the identification of words in text that correspond to a pre-defined taxonomy such as person, organization, location, date, time, etc.This article focuses on the person (PER), organization (ORG) and location (LOC) entities for a Malay journalistic corpus of terrorism.A projection algorithm, using the Dice Coefficient function and bigram scoring method with domain-specific rules, is suggested to map the NE information from the English corpus to the Malay corpus of terrorism.The English corpus is the translated version of the Malay corpus.Hence, these two corpora are treated as parallel corpora. The method computes the string similarity between the English words and the list of available lexemes in a pre-built lexicon that approximates the best NE mapping.The algorithm has been effectively evaluated using our own terrorism tagged corpus; it achieved satisfactory results in terms of precision, recall, and F-measure.An evaluation of the selected open source NER tool for English is also presented. Universiti Utara Malaysia Press 2012 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/24088/1/J%20ICT%2012%202013%20121%E2%80%93146.pdf Zamin, Norshuhani and Oxley, Alan and Abu Bakar, Zainab (2012) Projecting named entity tags from a resource rich language to a resource poor language. Journal of Information and Communication Technology, 11. pp. 121-146. ISSN 2180-3862 http://jict.uum.edu.my/index.php/previous-issues/141-journal-of-information-and-communication-technology-jict-vol-12-2013 |
| spellingShingle | QA75 Electronic computers. Computer science Zamin, Norshuhani Oxley, Alan Abu Bakar, Zainab Projecting named entity tags from a resource rich language to a resource poor language |
| title | Projecting named entity tags from a resource rich language to a resource poor language |
| title_full | Projecting named entity tags from a resource rich language to a resource poor language |
| title_fullStr | Projecting named entity tags from a resource rich language to a resource poor language |
| title_full_unstemmed | Projecting named entity tags from a resource rich language to a resource poor language |
| title_short | Projecting named entity tags from a resource rich language to a resource poor language |
| title_sort | projecting named entity tags from a resource rich language to a resource poor language |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/24088/1/J%20ICT%2012%202013%20121%E2%80%93146.pdf |
| work_keys_str_mv | AT zaminnorshuhani projectingnamedentitytagsfromaresourcerichlanguagetoaresourcepoorlanguage AT oxleyalan projectingnamedentitytagsfromaresourcerichlanguagetoaresourcepoorlanguage AT abubakarzainab projectingnamedentitytagsfromaresourcerichlanguagetoaresourcepoorlanguage |