| Item type |
SIG Technical Reports(1) |
| 公開日 |
2022-11-22 |
| タイトル |
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タイトル |
Link Prediction from Text Content by NLP Graph Embedding－A Study on Chinese Journal Articles－ |
| タイトル |
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言語 |
en |
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タイトル |
Link Prediction from Text Content by NLP Graph Embedding－A Study on Chinese Journal Articles－ |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
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主題 |
Web応用 |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
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資源タイプ |
technical report |
| 著者所属 |
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Department of East Asian Studies, National Taiwan Normal University |
| 著者所属 |
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Department of East Asian Studies, National Taiwan Normal University |
| 著者所属 |
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Department of East Asian Studies, National Taiwan Normal University |
| 著者所属 |
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College of Law, National Taiwan University |
| 著者所属(英) |
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en |
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Department of East Asian Studies, National Taiwan Normal University |
| 著者所属(英) |
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en |
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Department of East Asian Studies, National Taiwan Normal University |
| 著者所属(英) |
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en |
|
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Department of East Asian Studies, National Taiwan Normal University |
| 著者所属(英) |
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en |
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College of Law, National Taiwan University |
| 著者名 |
Tzu-Ying, Yang
Hsuan-Lei, Shao
Chih-Chuan, Fan
Wei-Hsin, Wang
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| 著者名(英) |
Tzu-Ying, Yang
Hsuan-Lei, Shao
Chih-Chuan, Fan
Wei-Hsin, Wang
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| 論文抄録 |
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内容記述タイプ |
Other |
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内容記述 |
This paper is an extended research of the project “The Knowledge Database / Graph of China-studies”. The main research target is to predict the new research stream from known journal papers by the graph embedding and link prediction. The challenge of our dataset does not include citation relationships; therefore, we might retrieve features of relationships from the content of the papers inside directly. We used keywords collaboration and k-means to reduce dimension, then word2vec and MLP to classify if any two nodes can link in the next round (year). Finally, we could achieve over 90% accuracy in each round which is better than the base-line method (random-forest with Adar and Jaccard score). And we also provide a visualization graph in action. We contribute a pipeline workflow to the rawer bibliography dataset which doesn’t conclude cite-relationship, and this workflow can be used on social media or other text-only datasets. |
| 論文抄録(英) |
|
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内容記述タイプ |
Other |
|
内容記述 |
This paper is an extended research of the project “The Knowledge Database / Graph of China-studies”. The main research target is to predict the new research stream from known journal papers by the graph embedding and link prediction. The challenge of our dataset does not include citation relationships; therefore, we might retrieve features of relationships from the content of the papers inside directly. We used keywords collaboration and k-means to reduce dimension, then word2vec and MLP to classify if any two nodes can link in the next round (year). Finally, we could achieve over 90% accuracy in each round which is better than the base-line method (random-forest with Adar and Jaccard score). And we also provide a visualization graph in action. We contribute a pipeline workflow to the rawer bibliography dataset which doesn’t conclude cite-relationship, and this workflow can be used on social media or other text-only datasets. |
| 書誌レコードID |
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収録物識別子タイプ |
NCID |
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収録物識別子 |
AN10442647 |
| 書誌情報 |
研究報告音声言語情報処理（SLP）
巻 2022-SLP-144,
号 1,
p. 1-4,
発行日 2022-11-22
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2188-8663 |
| Notice |
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SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. |
| 出版者 |
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言語 |
ja |
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出版者 |
情報処理学会 |
IPSJ-SLP22144001.pdf (1.3 MB)
