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Automatic Creation of Correspondence Table of Meaning Tags from Two Dictionaries in One Language Using Bilingual Word Embedding
https://doi.org/10.15084/00003069
https://doi.org/10.15084/000030697b6cb6b1-d0c3-4021-8e01-fbf3a4b838e3
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
2020.bucc-1.4.pdf (204.2 kB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||
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| 公開日 | 2020-12-18 | |||||
| タイトル | ||||||
| タイトル | Automatic Creation of Correspondence Table of Meaning Tags from Two Dictionaries in One Language Using Bilingual Word Embedding | |||||
| 言語 | en | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Bilingual Word Embedding | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Concept Embeddings | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Word Embeddings | |||||
| キーワード | ||||||
| 言語 | en | |||||
| 主題Scheme | Other | |||||
| 主題 | Dictionary | |||||
| 資源タイプ | ||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
| 資源タイプ | journal article | |||||
| ID登録 | ||||||
| ID登録 | 10.15084/00003069 | |||||
| ID登録タイプ | JaLC | |||||
| 著者 |
Hirabayashi, Teruo
× Hirabayashi, Teruo× Komiya, Kanako× Asahara, Masayuki× Shinnou, Hiroyuki |
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| 著者所属(英) | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Ibaraki University | |||||
| 著者所属(英) | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Ibaraki University | |||||
| 著者所属(英) | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | National Institute for Japanese Language and Linguistics | |||||
| 著者所属(英) | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Ibaraki University | |||||
| 抄録(英) | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | In this paper, we show how to use bilingual word embeddings (BWE) to automatically create a corresponding table of meaning tags from two dictionaries in one language and examine the effectiveness of the method. To do this, we had a problem: the meaning tags do not always correspond one-to-one because the granularities of the word senses and the concepts are different from each other. Therefore, we regarded the concept tag that corresponds to a word sense the most as the correct concept tag corresponding the word sense. We used two BWE methods, a linear transformation matrix and VecMap. We evaluated the most frequent sense (MFS) method and the corpus concatenation method for comparison. The accuracies of the proposed methods were higher than the accuracy of the random baseline but lower than those of the MFS and corpus concatenation methods. However, because our method utilized the embedding vectors of the word senses, the relations of the sense tags corresponding to concept tags could be examined by mapping the sense embeddings to the vector space of the concept tags. Also, our methods could be performed when we have only concept or word sense embeddings whereas the MFS method requires a parallel corpus and the corpus concatenation method needs two tagged corpora. | |||||
| 出版者 | ||||||
| 出版者 | European Language Resources Association | |||||
| 書誌情報 |
en : Proceedings of the 13th Workshop on Building and Using Comparable Corpora p. 22-28, 発行日 2020-05 |
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| フォーマット | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | application/pdf | |||||
| 著者版フラグ | ||||||
| 出版タイプ | VoR | |||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
