WEKO3
アイテム
Emotion Recognition for Japanese Short Sentences Including Slangs Based on Bag of Concepts Feature Trained by Large Web Text
https://tokushima-u.repo.nii.ac.jp/records/2006356
https://tokushima-u.repo.nii.ac.jp/records/200635699ececb9-36e0-4530-b523-080d289c9976
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
caic_2019_2_9.pdf (788 KB)
|
.png)
|
| Item type | 文献 / Documents(1) | |||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2019-05-08 | |||||||||||||||||||||||
| アクセス権 | ||||||||||||||||||||||||
| アクセス権 | open access | |||||||||||||||||||||||
| 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 | |||||||||||||||||||||||
| 出版タイプ | ||||||||||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||||||||
| タイトル | ||||||||||||||||||||||||
| タイトル | Emotion Recognition for Japanese Short Sentences Including Slangs Based on Bag of Concepts Feature Trained by Large Web Text | |||||||||||||||||||||||
| タイトル別表記 | ||||||||||||||||||||||||
| その他のタイトル | Emotion Recognition for Japanese Short Sentences Including Slangs | |||||||||||||||||||||||
| 著者 |
松本, 和幸
× 松本, 和幸
WEKO
311
× 吉田, 稔
WEKO
641
× 北, 研二
WEKO
94
|
|||||||||||||||||||||||
| 抄録 | ||||||||||||||||||||||||
| 内容記述 | The growth of Internet communication sites such as weblogs and social networking sites brought younger people especially in teens and in their 20s to create new words and to use them very often. We prepared an emotion corpus by collecting weblog article texts including new words, analyzed the corpus statistically, and proposed a method to estimate emotions of the texts. Most slang words such as Youth Slang are too ambiguous in sense classification to be registered into the existing dictionaries such as thesaurus. To cope with these words, we created a large scale of Twitter corpus and calculated sense similarities between words. We proposed to convert unknown word to semantic class id so that we might be able to process the words that were not included in the learning data. For calculation similarities between words and converting the word into word cluster id, we used the word embedding algorithms such as word2vec, or GloVe. We defined this method as a method using Bag of Concepts as feature. As a result of the evaluation experiment using several classifiers, the proposed method was proved its robustness for unknown expressions. | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | Youth Slang | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | Unknown Words | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | Bag of Concepts | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | Word Embedding | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | k-nearest neighbor algorithm | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | Maximum Entropy Method | |||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||
| 主題 | Unsupervised Clustering | |||||||||||||||||||||||
| 書誌情報 |
en : Current Analysis on Instrumentation and Control 巻 2019, 号 2, p. 9-18, 発行日 2019-02-01 |
|||||||||||||||||||||||
| 出版者 | ||||||||||||||||||||||||
| 出版者 | Mesford Publisher | |||||||||||||||||||||||
| 権利情報 | ||||||||||||||||||||||||
| 権利情報 | This is an open access article licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (Attribution-NonCommercial 4.0 International CC-BY-NC 4.0)(https://creativecommons.org/licenses/by-nc/4.0/deed.ja) © 2018 Mesford Publisher INC | |||||||||||||||||||||||
| EID | ||||||||||||||||||||||||
| 識別子 | 349336 | |||||||||||||||||||||||
| 言語 | ||||||||||||||||||||||||
| 言語 | eng | |||||||||||||||||||||||
