Named Entity Recognition System for Postpositional Languages: Urdu as a Case Study
Named Entity Recognition and Classification is the process of identifying named entities and classifying them into one of the classes like person name, organization name, location name, etc. In this paper, we propose a tagging scheme Begin Inside Last -2 (BIL2) for the Subject Object Verb (SOV) lang...
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| Published in | International journal of advanced computer science & applications Vol. 7; no. 10 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
West Yorkshire
Science and Information (SAI) Organization Limited
01.01.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2158-107X 2156-5570 2156-5570 |
| DOI | 10.14569/IJACSA.2016.071019 |
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| Abstract | Named Entity Recognition and Classification is the process of identifying named entities and classifying them into one of the classes like person name, organization name, location name, etc. In this paper, we propose a tagging scheme Begin Inside Last -2 (BIL2) for the Subject Object Verb (SOV) languages that contain postposition. We use the Urdu language as a case study. We compare the F-measure values obtained for the tagging schemes IO, BIO2, BILOU and BIL2 using Hidden Markov Model (HMM) and Conditional Random Field (CRF). The BIL2 tagging scheme results are better than the other three tagging schemes using the same parameters including bigram and context window. With HMM, the F-measure values for IO, BIO2, BILOU, and BIL2 are 44.87%, 44.88%, 45.14%, and 45.88%, respectively. With CRF, the F-measure values for IO, BIO2, BILOU, and BIL2 are 35.13%, 35.90%, 37.85%, and 38.39%, respectively. The F-measure values for BIL2 are better than those of previously reported techniques |
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| AbstractList | Named Entity Recognition and Classification is the process of identifying named entities and classifying them into one of the classes like person name, organization name, location name, etc. In this paper, we propose a tagging scheme Begin Inside Last -2 (BIL2) for the Subject Object Verb (SOV) languages that contain postposition. We use the Urdu language as a case study. We compare the F-measure values obtained for the tagging schemes IO, BIO2, BILOU and BIL2 using Hidden Markov Model (HMM) and Conditional Random Field (CRF). The BIL2 tagging scheme results are better than the other three tagging schemes using the same parameters including bigram and context window. With HMM, the F-measure values for IO, BIO2, BILOU, and BIL2 are 44.87%, 44.88%, 45.14%, and 45.88%, respectively. With CRF, the F-measure values for IO, BIO2, BILOU, and BIL2 are 35.13%, 35.90%, 37.85%, and 38.39%, respectively. The F-measure values for BIL2 are better than those of previously reported techniques |
| Author | Mansoor, Syed Kamran, Muhammad |
| Author_xml | – sequence: 1 givenname: Muhammad surname: Kamran fullname: Kamran, Muhammad – sequence: 2 givenname: Syed surname: Mansoor fullname: Mansoor, Syed |
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| Copyright | 2016. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Title | Named Entity Recognition System for Postpositional Languages: Urdu as a Case Study |
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