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NLP for Shallow Question Answering of Legal Documents Using Graphs

机译:NLP使用图形对法律文件进行浅层问答

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Previous work has shown that modeling relationships between articles of a regulation as vertices of a graph network works twice as better than traditional information retrieval systems for returning articles relevant to the question. In this work we experiment by using natural language techniques such as lemmatizing and using manual and automatic thesauri for improving question based document retrieval. For the construction of the graph, we follow the approach of representing the set of all the articles as a graph; the question is split in two parts, and each of them is added as part of the graph. Then several paths are constructed from part A of the question to part B, so that the shortest path contains the relevant articles to the question. We evaluate our method comparing the answers given by a traditional information retrieval system-vector space model adjusted for article retrieval, instead of document retrieval-and the answers to 21 questions given manually by the general lawyer of the National Polytechnic Institute, based on 25 different regulations (academy regulation, scholarships regulation, postgraduate studies regulation, etc.); with the answer of our system based on the same set of regulations. We found that lemmatizing increases performance in around 10%, while the use of thesaurus has a low impact.
机译:先前的工作表明,将规章条款之间的关系建模为图形网络的顶点,其工作效率比传统的信息检索系统高出两倍,该系统用于返回与问题相关的条款。在这项工作中,我们尝试使用自然语言技术(例如lemmatization)以及使用手动和自动叙词表来改进基于问题的文档检索。对于图的构建,我们采用将所有商品的集合表示为图的方法。该问题分为两个部分,每个部分都作为图的一部分添加。然后,构建了从问题的A部分到B部分的几种路径,以便最短的路径包含该问题的相关文章。我们评估了我们的方法,根据25种不同的方法,比较了传统信息检索系统给出的答案-调整为文章检索而不是文档检索的向量空间模型-以及国家职业技术学院总律师手动给出的21个问题的答案(基于25种不同法规(学院法规,奖学金法规,研究生课程法规等);并基于同一套规定对我们系统的回答。我们发现去词皮化可以将性能提高10%左右,而使用同义词库的影响很小。

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