Multi-granularity Argument Mining in Legal Texts

• URL: http://arxiv.org/abs/2210.09472v2
• Date: Wed, 19 Oct 2022 15:00:55 GMT
• Title: Multi-granularity Argument Mining in Legal Texts
• Authors: Huihui Xu and Kevin Ashley
• Abstract summary: We conceptualize argument mining as a token-level (i.e., word-level) classification problem. Results show that token-level text classification identifies certain legal argument elements more accurately than sentence-level text classification.
• Score: 0.913755431537592
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we explore legal argument mining using multiple levels of granularity. Argument mining has usually been conceptualized as a sentence classification problem. In this work, we conceptualize argument mining as a token-level (i.e., word-level) classification problem. We use a Longformer model to classify the tokens. Results show that token-level text classification identifies certain legal argument elements more accurately than sentence-level text classification. Token-level classification also provides greater flexibility to analyze legal texts and to gain more insight into what the model focuses on when processing a large amount of input data.

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