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JACIII Vol.28 No.2 pp. 239-254
doi: 10.20965/jaciii.2024.p0239
(2024)

Research Paper:

A Comparative Study of Relation Classification Approaches for Japanese Discourse Relation Analysis

Keigo Takahashi* ORCID Icon, Teruaki Oka* ORCID Icon, Mamoru Komachi** ORCID Icon, and Yasufumi Takama* ORCID Icon

*Graduate School of Systems Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

**Graduate School of Social Data Science, Hitotsubashi University
2-1 Naka, Kunitachi, Tokyo 186-8601, Japan

Received:
August 23, 2023
Accepted:
October 2, 2023
Published:
March 20, 2024
Keywords:
natural language processing, discourse relation analysis, special token, Japanese
Abstract

This paper presents a comparative analysis of classification approaches in the Japanese discourse relation analysis (DRA) task. In the Japanese DRA task, it is difficult to resolve implicit relations where explicit discourse phrases do not appear. To understand implicit relations further, we compared the four approaches by incorporating a special token to encode the relations of the given discourses. Our four approaches included inserting a special token at the beginning of a sentence, end of a sentence, conjunctive position, and random position to classify the relation between the two discourses into one of the following categories: CAUSE/REASON, CONCESSION, CONDITION, PURPOSE, GROUND, CONTRAST, and NONE. Our experimental results revealed that special tokens are available to encode the relations of given discourses more effectively than pooling-based approaches. In particular, the random insertion of a special token outperforms other approaches, including pooling-based approaches, in the most numerous CAUSE/REASON category in implicit relations and categories with few instances. Moreover, we classified the errors in the relation analysis into three categories: confounded phrases, ambiguous relations, and requiring world knowledge for further improvements.

Sp. token shows performance and robustness

Sp. token shows performance and robustness

Cite this article as:
K. Takahashi, T. Oka, M. Komachi, and Y. Takama, “A Comparative Study of Relation Classification Approaches for Japanese Discourse Relation Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.2, pp. 239-254, 2024.
Data files:
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Last updated on Apr. 05, 2024