NLP for Shallow Question Answering of Legal Documents Using Graphs
NLP for Shallow Question Answering of Legal Documents Using Graphs
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DOI：
10.1007/978-3-642-00382-0_40
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发表时间：
2009-02
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影响因子：
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通讯作者：
Alfredo Monroy;Hiram Calvo;Alexander Gelbukh
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文献类型：
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作者：
Alfredo Monroy;Hiram Calvo;Alexander Gelbukh
以前的工作已经表明，模型之间的关系，一个条例的顶点图网络的作品比传统的信息检索系统返回的文章相关的问题的两倍。在这项工作中，我们实验使用自然语言技术，如lemmatizing和使用手动和自动词库，以提高基于问题的文档检索。对于图的构造，我们遵循将所有文章的集合表示为图的方法;问题被分成两部分，并且每个部分都被添加为图的一部分。然后从问题的A部分到B部分构造几条路径，使得最短路径包含到问题的相关文章。我们评估我们的方法比较的答案由传统的信息检索系统向量空间模型调整的文章检索，而不是文件检索和回答21个问题的一般律师的国立理工学院，根据25个不同的法规（学院规定，奖学金规定，研究生学习规定等）;我们的系统基于同样的规则。我们发现，词形化提高了大约10%的性能，而使用同义词词典的影响很小。
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.