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Keyphrase-based Refinement Functions for Efficient Improvement on Document-Topic Association in Human-in-the-Loop Topic Models
https://ipsj.ixsq.nii.ac.jp/records/225589
https://ipsj.ixsq.nii.ac.jp/records/2255896b2aad7a-838b-43b4-ad5b-d334f03a1ff1
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-TOD1602006.pdf (1.7 MB)
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Copyright (c) 2023 by the Information Processing Society of Japan
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| オープンアクセス | ||
| Item type | Trans(1) | |||||||||
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| 公開日 | 2023-04-14 | |||||||||
| タイトル | ||||||||||
| タイトル | Keyphrase-based Refinement Functions for Efficient Improvement on Document-Topic Association in Human-in-the-Loop Topic Models | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Keyphrase-based Refinement Functions for Efficient Improvement on Document-Topic Association in Human-in-the-Loop Topic Models | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | [研究論文] topic models, latent dirichlet allocation, human-in-the-loop topic modeling, keyphrase generation model | |||||||||
| 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 | |||||||||
| 著者所属 | ||||||||||
| University of Tsukuba | ||||||||||
| 著者所属 | ||||||||||
| University of Tsukuba | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| University of Tsukuba | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| University of Tsukuba | ||||||||||
| 著者名 |
Khan, Muhammad Haseeb Ur Rehman
× Khan, Muhammad Haseeb Ur Rehman
× Kei, Wakabayashi
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| 著者名(英) |
Khan, Muhammad Haseeb Ur Rehman
× Khan, Muhammad Haseeb Ur Rehman
× Kei, Wakabayashi
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Human-in-the-loop topic models allow users to encode feedback to modify topic models without changing the core machinery of the topic models. Basic refinement functions have been proposed in prior works in which the main focus was to modify the top word lists of topics, e.g., add a single word in a topic having distribution over a large vocabulary set. In this work, we point out that such refinements have very little to no effect on document-topic associations, which are rather important in practical applications, and propose keyphrase-based refinement functions that are designed to improve document-topic associations efficiently. In the proposed method, these keyphrases are extracted by using a neural keyphrase generation model that summarizes a document in a few keyphrases which are human-interpretable representations of each given document. The proposed refinement functions are as simple as word-based refinements but directly modify the document-topic association in functionality by referring to the keyphrase representations of documents. To examine the capability of the refinement functions for revising topic models, we conducted experiments based on a simulated user that has a fixed preference for the document-topic association using 20Newsgroups dataset. Our results showed that the proposed keyphrase-based refinements outperform the basic word-based refinements in terms of the F1 score computed on the fixed preference. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.31(2023) (online) ------------------------------ |
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| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Human-in-the-loop topic models allow users to encode feedback to modify topic models without changing the core machinery of the topic models. Basic refinement functions have been proposed in prior works in which the main focus was to modify the top word lists of topics, e.g., add a single word in a topic having distribution over a large vocabulary set. In this work, we point out that such refinements have very little to no effect on document-topic associations, which are rather important in practical applications, and propose keyphrase-based refinement functions that are designed to improve document-topic associations efficiently. In the proposed method, these keyphrases are extracted by using a neural keyphrase generation model that summarizes a document in a few keyphrases which are human-interpretable representations of each given document. The proposed refinement functions are as simple as word-based refinements but directly modify the document-topic association in functionality by referring to the keyphrase representations of documents. To examine the capability of the refinement functions for revising topic models, we conducted experiments based on a simulated user that has a fixed preference for the document-topic association using 20Newsgroups dataset. Our results showed that the proposed keyphrase-based refinements outperform the basic word-based refinements in terms of the F1 score computed on the fixed preference. ------------------------------ This is a preprint of an article intended for publication Journal of Information Processing(JIP). This preprint should not be cited. This article should be cited as: Journal of Information Processing Vol.31(2023) (online) ------------------------------ |
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| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AA11464847 | |||||||||
| 書誌情報 |
情報処理学会論文誌データベース（TOD） 巻 16, 号 2, 発行日 2023-04-14 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 1882-7799 | |||||||||
| 出版者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
