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http://dx.doi.org/10.9728/dcs.2017.18.7.1419

A Study on the Law2Vec Model for Searching Related Law  

Kim, Nari (Department of Big Data Application and Security, Korea University)
Kim, Hyoung Joong (Department of Big Data Application and Security, Korea University)
Publication Information
Journal of Digital Contents Society / v.18, no.7, 2017 , pp. 1419-1425 More about this Journal
Abstract
The ultimate goal of legal knowledge search is to obtain optimal legal information based on laws and precedent. Text mining research is actively being undertaken to meet the needs of efficient retrieval from large scale data. A typical method is to use a word embedding algorithm based on Neural Net. This paper demonstrates how to search relevant information, applying Korean law information to word embedding. First, we extracts reference laws from precedents in order and takes reference laws as input of Law2Vec. The model learns a law by predicting its surrounding context law. The algorithm then moves over each law in the corpus and repeats the training step. After the training finished, we could infer the relationship between the laws via the embedding method. The search performance was evaluated based on precision and the recall rate which are computed from how closely the results are associated to the search terms. The test result proved that what this paper proposes is much more useful compared to existing systems utilizing only keyword search when it comes to extracting related laws.
Keywords
Text Mining; Legal Tech; Machine Learning; Word Embedding; Word2Vec; Keyword;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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