论文标题
法律文本中的多跨性论点挖掘
Multi-granularity Argument Mining in Legal Texts
论文作者
论文摘要
在本文中，我们使用多个颗粒状探索法律论证挖掘。参数挖掘通常被概念化为句子分类问题。在这项工作中，我们将论点挖掘概念化为代币级别（即单词级）分类问题。我们使用longformer模型对令牌进行分类。结果表明，令牌级文本分类比句子级文本分类更准确地标识某些法律论点元素。令牌级别的分类还提供了更大的灵活性来分析法律文本，并更深入地了解该模型在处理大量输入数据时的关注。
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.
