Deep Learning Based Semantic Similarity for Korean Legal Field


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 2, pp. 93-100, Feb. 2022
https://doi.org/10.3745/KTSDE.2022.11.2.93,   PDF Download:
Keywords: NLP, LegalTech, Semantic similarity, BERT, Legal
Abstract

Keyword-oriented search methods are mainly used as data search methods, but this is not suitable as a search method in the legal field where professional terms are widely used. In response, this paper proposes an effective data search method in the legal field. We describe embedding methods optimized for determining similarities between sentences in the field of natural language processing of legal domains. After embedding legal sentences based on keywords using TF-IDF or semantic embedding using Universal Sentence Encoder, we propose an optimal way to search for data by combining BERT models to check similarities between sentences in the legal field.


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Cite this article
[IEEE Style]
S. W. Kim and G. R. Park, "Deep Learning Based Semantic Similarity for Korean Legal Field," KIPS Transactions on Software and Data Engineering, vol. 11, no. 2, pp. 93-100, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.2.93.

[ACM Style]
Sung Won Kim and Gwang Ryeol Park. 2022. Deep Learning Based Semantic Similarity for Korean Legal Field. KIPS Transactions on Software and Data Engineering, 11, 2, (2022), 93-100. DOI: https://doi.org/10.3745/KTSDE.2022.11.2.93.