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Hierarchical neural network: Integrate divide-and-conquer and unified approach for argument unit recognition and classification.
- Source :
-
Information Sciences . May2023, Vol. 624, p796-810. 15p. - Publication Year :
- 2023
-
Abstract
- Argument unit recognition and classification (AURC) is a promising and critical research topic in argument mining, which aims to extract the argument units that express support or opposing stance in a given argumentative text under controversial topics. Existing studies treated the AURC as a sequence labeling problem and designed a unified approach to predict argument unit boundary and argument unit stance simultaneously. In this paper, we propose a general framework h ierarchical n eural n etwork (HNN) for AURC, by fusing two different approach: divide-and-conquer approach and unified approach. The divide-and-conquer approach considers the correlation of the two tasks inherent in AURC (task 1: argument unit recognition, AUR and task 2: argument unit classification, AUC), and jointly optimize them for prediction by a novel probability transition matrix. Finally, we used a token-level attention mechanism to efficiently fuse probability distributions obtained by our proposed divide-and-conquer approach and existing unified approach. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed framework. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ARGUMENT
*DISTRIBUTION (Probability theory)
*LABEL design
Subjects
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 624
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 161904749
- Full Text :
- https://doi.org/10.1016/j.ins.2022.12.050