Khmer POS Tagging Using Conditional Random Fields
© 2018, Springer Nature Singapore Pte Ltd. The transformation-based approach with hybrid of rule-based and tri-gram have already been introduced for Khmer part-of-speech (POS) tagging. In this study, in order to further explore this topic, we present an alternative approach to Khmer POS tagging usin...
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th-mahidol.456692019-08-23T18:31:01Z Khmer POS Tagging Using Conditional Random Fields Sokunsatya Sangvat Charnyote Pluempitiwiriyawej Mahidol University Computer Science Mathematics © 2018, Springer Nature Singapore Pte Ltd. The transformation-based approach with hybrid of rule-based and tri-gram have already been introduced for Khmer part-of-speech (POS) tagging. In this study, in order to further explore this topic, we present an alternative approach to Khmer POS tagging using Conditional Random Fields (CRFs). Since the features greatly affect the tagging accuracy, we investigate five groups of features and use them with the CRF model. First, we study different contextual information and use it as our baseline model. We then analyze the characteristics of Khmer and come up with three additional groups of language-related features including morphemes, word-shapes and name-entities. We also explore the use of lexicon as features to further improve the accuracy of our tagger. Our proposed approach has been evaluated on a corpus of 41,058 words and 27 POS tags. The comparative study has shown that our proposed approach produces a competitive accuracy compared to other Khmer POS tagging approaches. 2019-08-23T10:58:35Z 2019-08-23T10:58:35Z 2018-01-01 Conference Paper Communications in Computer and Information Science. Vol.781, (2018), 169-178 10.1007/978-981-10-8438-6_14 18650929 2-s2.0-85044073164 https://repository.li.mahidol.ac.th/handle/123456789/45669 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85044073164&origin=inward |
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Computer Science Mathematics Sokunsatya Sangvat Charnyote Pluempitiwiriyawej Khmer POS Tagging Using Conditional Random Fields |
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© 2018, Springer Nature Singapore Pte Ltd. The transformation-based approach with hybrid of rule-based and tri-gram have already been introduced for Khmer part-of-speech (POS) tagging. In this study, in order to further explore this topic, we present an alternative approach to Khmer POS tagging using Conditional Random Fields (CRFs). Since the features greatly affect the tagging accuracy, we investigate five groups of features and use them with the CRF model. First, we study different contextual information and use it as our baseline model. We then analyze the characteristics of Khmer and come up with three additional groups of language-related features including morphemes, word-shapes and name-entities. We also explore the use of lexicon as features to further improve the accuracy of our tagger. Our proposed approach has been evaluated on a corpus of 41,058 words and 27 POS tags. The comparative study has shown that our proposed approach produces a competitive accuracy compared to other Khmer POS tagging approaches. |
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Mahidol University |
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Mahidol University Sokunsatya Sangvat Charnyote Pluempitiwiriyawej |
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Conference or Workshop Item |
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Sokunsatya Sangvat Charnyote Pluempitiwiriyawej |
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Sokunsatya Sangvat |
title |
Khmer POS Tagging Using Conditional Random Fields |
title_short |
Khmer POS Tagging Using Conditional Random Fields |
title_full |
Khmer POS Tagging Using Conditional Random Fields |
title_fullStr |
Khmer POS Tagging Using Conditional Random Fields |
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Khmer POS Tagging Using Conditional Random Fields |
title_sort |
khmer pos tagging using conditional random fields |
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2019 |
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https://repository.li.mahidol.ac.th/handle/123456789/45669 |
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