Domain specific information retrieval process has been a prominent and
ongoing research in the field of natural language processing. Many researchers
have incorporated different techniques to overcome the technical and domain
specificity and provide a mature model for various domains of interest. The
main bottleneck in these studies is the heavy coupling of domain experts, that
makes the entire process to be time consuming and cumbersome. In this study, we
have developed three novel models which are compared against a golden standard
generated via the on line repositories provided, specifically for the legal
domain. The three different models incorporated vector space representations of
the legal domain, where document vector generation was done in two different
mechanisms and as an ensemble of the above two. This study contains the
research being carried out in the process of representing legal case documents
into different vector spaces, whilst incorporating semantic word measures and
natural language processing techniques. The ensemble model built in this study,
shows a significantly higher accuracy level, which indeed proves the need for
incorporation of domain specific semantic similarity measures into the
information retrieval process. This study also shows, the impact of varying
distribution of the word similarity measures, against varying document vector
dimensions, which can lead to improvements in the process of legal information
retrieval.

本研究使用三种模型，分别基于向量空间表示法，将法律案例文档表示为不同的向量空间，并结合语义词度量和自然语言处理技术，展示了在信息检索过程中引入领域特定语义相似性测量的必要性，表明整合多种技术可显著提高信息检索的准确性，同时还展示了词相似度测量分布变化及文档向量维度变化对法律信息检索过程的影响，从而为信息检索提供了更好的方案。