论文标题
使用预先训练的短语评分模型建立法律案例检索系统，并通过词汇匹配和汇总
Building Legal Case Retrieval Systems with Lexical Matching and Summarization using A Pre-Trained Phrase Scoring Model
论文作者
论文摘要
我们介绍了解决法律信息提取/2019年法律案件检索任务的方法。我们的方法基于这样的想法：摘要对于检索至关重要。一方面，我们采用了一个基于摘要的模型，称为编码摘要，该模型将给定文档编码到连续的向量空间中，该文档嵌入了文档的摘要属性。我们利用了Coliee 2018的资源，我们在该资源上训练文档表示模型。另一方面，我们在给定查询及其候选人的不同部分中提取词汇特征。我们观察到，通过比较查询及其候选人的不同部分，我们可以取得更好的性能。此外，通过基于摘要的方法，词汇特征与潜在特征的组合可以实现更好的性能。我们已经在比赛基准上实现了任务的最新结果。
We present our method for tackling the legal case retrieval task of the Competition on Legal Information Extraction/Entailment 2019. Our approach is based on the idea that summarization is important for retrieval. On one hand, we adopt a summarization based model called encoded summarization which encodes a given document into continuous vector space which embeds the summary properties of the document. We utilize the resource of COLIEE 2018 on which we train the document representation model. On the other hand, we extract lexical features on different parts of a given query and its candidates. We observe that by comparing different parts of the query and its candidates, we can achieve better performance. Furthermore, the combination of the lexical features with latent features by the summarization-based method achieves even better performance. We have achieved the state-of-the-art result for the task on the benchmark of the competition.
