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Legal Information Retrieval Using Topic Clustering and Neural Networks

11 pagesPublished: June 3, 2017

Abstract

This paper presents a description about our adopted approach for the information retrieval and textual entailment tasks of the COLIEE 2017 competition. We address the information retrieval task by implementing a partial string matching and a topic clustering method. For the textual entailment task, we propose a Long Short-Term Memory (LSTM) - Convolutional Neural Network (CNN) model which utilizes word embeddings trained on the Google News vectors. We evaluated our approach for both tasks on the COLIEE 2017 dataset. The results demonstrate that the topic clustering method outperformed the partial string matching method in the information retrieval task. The performance of LSTM-CNN model was competitive with other textual entailment systems.

Keyphrases: Information Retrieval, neural networks, Textual Entailment, topic clustering

In: Ken Satoh, Mi-Young Kim, Yoshinobu Kano, Randy Goebel and Tiago Oliveira (editors). COLIEE 2017. 4th Competition on Legal Information Extraction and Entailment, vol 47, pages 68--78

Links:
BibTeX entry
@inproceedings{COLIEE2017:Legal_Information_Retrieval_Using,
  author    = {Rohan Nanda and Adebayo Kolawole John and Luigi Di Caro and Guido Boella and Livio Robaldo},
  title     = {Legal Information Retrieval Using Topic Clustering and Neural Networks},
  booktitle = {COLIEE 2017. 4th Competition on Legal Information Extraction and Entailment},
  editor    = {Ken Satoh and Mi-Young Kim and Yoshinobu Kano and Randy Goebel and Tiago Oliveira},
  series    = {EPiC Series in Computing},
  volume    = {47},
  pages     = {68--78},
  year      = {2017},
  publisher = {EasyChair},
  bibsource = {EasyChair, http://www.easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/RC},
  doi       = {10.29007/psgx}}
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