Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation
Word sense disambiguation (WSD) is a task of determining a reasonable sense of a word in a particular context. Although recent studies have demonstrated some progress in the advancement of neural language models, the scope of research is still such that the senses of several words can only be determ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8976181/ |
| _version_ | 1818857165159399424 |
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| author | Yoonseok Heo Sangwoo Kang Jungyun Seo |
| author_facet | Yoonseok Heo Sangwoo Kang Jungyun Seo |
| author_sort | Yoonseok Heo |
| collection | DOAJ |
| description | Word sense disambiguation (WSD) is a task of determining a reasonable sense of a word in a particular context. Although recent studies have demonstrated some progress in the advancement of neural language models, the scope of research is still such that the senses of several words can only be determined in a few domains. Therefore, it is necessary to move toward developing a highly scalable process that can address a lot of senses occurring in various domains. This paper introduces a new large WSD dataset that is automatically constructed from the Oxford Dictionary, which is widely used as a standard source for the meaning of words. We propose a new WSD model that individually determines the sense of the word in accordance with its part of speech in the context. In addition, we introduce a hybrid sense prediction method that separately classifies the less frequently used senses for achieving a reasonable performance. We have conducted comparative experiments to demonstrate that the proposed method is more reliable compared with the baseline approaches. Also, we investigated the adaptation of the method to a realistic environment with the use of news articles. |
| first_indexed | 2024-12-19T08:36:03Z |
| format | Article |
| id | doaj.art-dbb398f9072d415cad83de7562f4a0ff |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:36:03Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-dbb398f9072d415cad83de7562f4a0ff2022-12-21T20:29:03ZengIEEEIEEE Access2169-35362020-01-018272472725610.1109/ACCESS.2020.29704368976181Hybrid Sense Classification Method for Large-Scale Word Sense DisambiguationYoonseok Heo0https://orcid.org/0000-0002-4480-6415Sangwoo Kang1https://orcid.org/0000-0002-0281-1726Jungyun Seo2https://orcid.org/0000-0003-3670-7334Department of Computer Science and Engineering, Sogang University, Seoul, South KoreaDepartment of Software, Gachon University, Gyeonggi, South KoreaDepartment of Computer Science and Engineering, Sogang University, Seoul, South KoreaWord sense disambiguation (WSD) is a task of determining a reasonable sense of a word in a particular context. Although recent studies have demonstrated some progress in the advancement of neural language models, the scope of research is still such that the senses of several words can only be determined in a few domains. Therefore, it is necessary to move toward developing a highly scalable process that can address a lot of senses occurring in various domains. This paper introduces a new large WSD dataset that is automatically constructed from the Oxford Dictionary, which is widely used as a standard source for the meaning of words. We propose a new WSD model that individually determines the sense of the word in accordance with its part of speech in the context. In addition, we introduce a hybrid sense prediction method that separately classifies the less frequently used senses for achieving a reasonable performance. We have conducted comparative experiments to demonstrate that the proposed method is more reliable compared with the baseline approaches. Also, we investigated the adaptation of the method to a realistic environment with the use of news articles.https://ieeexplore.ieee.org/document/8976181/Computational and artificial intelligenceEnglish vocabulary learningnatural language processingneural networksword sense disambiguation |
| spellingShingle | Yoonseok Heo Sangwoo Kang Jungyun Seo Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation IEEE Access Computational and artificial intelligence English vocabulary learning natural language processing neural networks word sense disambiguation |
| title | Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation |
| title_full | Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation |
| title_fullStr | Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation |
| title_full_unstemmed | Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation |
| title_short | Hybrid Sense Classification Method for Large-Scale Word Sense Disambiguation |
| title_sort | hybrid sense classification method for large scale word sense disambiguation |
| topic | Computational and artificial intelligence English vocabulary learning natural language processing neural networks word sense disambiguation |
| url | https://ieeexplore.ieee.org/document/8976181/ |
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