Multi-label text classification for risk prediction in contracts
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- FUJII Mina
- GVA TECH K.K.
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- ABE Tomohiko
- GVA TECH K.K.
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- TAKAHASHI Koji
- GVA TECH K.K.
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- IWAKI Yasuhiro
- GVA TECH K.K.
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- KATO Tsuneaki
- Graduate School of Arts and Sciences, The University of Tokyo
Bibliographic Information
- Other Title
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- 契約書のリスク判定のための条文マルチラベル分類
Abstract
<p>To determine valid criteria in detecting risks of contracts is essential for automation of legal tasks such as reviewing contracts. In this paper, we propose a multi-label text classification with a neural network model in order to predict multiple review points in each clause of contracts. On our dataset consisting of over 20k Japanese contracts, in which each clause has 1 ~ 4 label(s) and the labels total 205, our model achieved 31 ~ 64 % accuracy, depending on the number of labels an input text contains, for test data. In addition, we observed probability transition from the first character to the last character of the input texts, character by character, to check the relation between input token and output labels, and we found out that this observation helps us to see where on input texts our model attends to predict labels.</p>
Journal
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- Proceedings of the Annual Conference of JSAI
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Proceedings of the Annual Conference of JSAI JSAI2020 (0), 4P3OS802-4P3OS802, 2020
The Japanese Society for Artificial Intelligence
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Keywords
Details 詳細情報について
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- CRID
- 1390848250119787008
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- NII Article ID
- 130007857335
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- Text Lang
- ja
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- Data Source
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- JaLC
- CiNii Articles
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- Abstract License Flag
- Disallowed