Legal Question Answering Using Ranking SVM and Syntactic/Semantic Similarity
Legal Question Answering Using Ranking SVM and Syntactic/Semantic Similarity
复制标题
使用排序 SVM 和句法/语义相似性进行法律问答
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
R. Goebel
中科院分区：
文献类型：
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作者：
Mi;Ying Xu;R. Goebel
介绍了一种集法律信息检索和文本蕴涵于一体的法律问答系统。我们使用2014年首届法律信息提取/蕴意竞赛（COLIEE）的数据评估了我们的系统。比赛主要集中在日本律师资格考试中回答是/否问题的法律信息处理两个方面。共享任务包括两个阶段：合法的临时信息检索和文本蕴涵。第一阶段需要对日本民法相关条文进行鉴定，进行司法考试查询。我们为该任务实现了两个无监督基线模型（tf-idf和基于潜在狄利克雷分配（LDA）的信息检索（IR））和一个监督模型（Ranking SVM）。该模型的特征是一组单词，以及基于相应基线模型的文章分数。结果表明，与两种基线模型相比，排序支持向量机模型的平均精度提高了近一倍。第二阶段是通过将查询的含义与相关文章进行比较，对以前未见过的查询回答“是”或“否”。第二阶段使用的特征是句法/语义相似性和否定/反义词关系的识别。结果表明，该方法将基于规则的模型和无监督模型相结合，优于基于支持向量机的有监督模型。
We describe a legal question answering system which combines legal information retrieval and textual entailment. We have evaluated our system using the data from the first competition on legal information extraction/entailment (COLIEE) 2014. The competition focuses on two aspects of legal information processing related to answering yes/no questions from Japanese legal bar exams. The shared task consists of two phases: legal ad hoc information retrieval and textual entailment. The first phase requires the identification of Japan civil law articles relevant to a legal bar exam query. We have implemented two unsupervised baseline models (tf-idf and Latent Dirichlet Allocation (LDA)-based Information Retrieval (IR)), and a supervised model, Ranking SVM, for the task. The features of the model are a set of words, and scores of an article based on the corresponding baseline models. The results show that the Ranking SVM model nearly doubles the Mean Average Precision compared with both baseline models. The second phase is to answer “Yes” or “No” to previously unseen queries, by comparing the meanings of queries with relevant articles. The features used for phase two are syntactic/semantic similarities and identification of negation/antonym relations. The results show that our method, combined with rule-based model and the unsupervised model, outperforms the SVM-based supervised model.