@InProceedings{tran:2017:RANLPStud,
  author    = {Tran, Anh Hang Nga},
  title     = {Applying Deep Neural Network to Retrieve Relevant Civil Law Articles},
  booktitle = {Proceedings of the Student Research Workshop Associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna},
  publisher = {INCOMA Ltd.},
  pages     = {46--48},
  abstract  = {The paper aims to achieve the legal question answering information
	retrieval (IR) task at Competition on Legal Information
	Extraction/Entailment (COLIEE) 2017. Our proposal methodology for the
	task is to utilize deep neural network, natural language processing and
	word2vec. The system was evaluated using training and testing data from
	the competition on legal information extraction/entailment (COLIEE).
	Our system mainly focuses on giving relevant civil law articles for given
	bar exams. The corpus of legal questions is drawn from Japanese Legal
	Bar exam queries. We implemented a combined deep neural network with
	additional features NLP and word2vec to gain the corresponding civil law
	articles based on a given bar exam 'Yes/No' questions. This paper focuses
	on clustering words-with- relation in order to acquire relevant civil law
	articles. All evaluation processes were done on the COLIEE 2017 training
	and test data set. The experimental result shows a very promising result.},
  url       = {https://doi.org/10.26615/issn.1314-9156.2017_007}
}