Detecting Text Semantic Similarity by Siamese Neural Networks with MaLSTM in Thai Language

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Publication Details

Author list: Poksappaiboon, Natkanok; Sundarabhogin, Nathaphop; Tungruethaipak, Natthawat; Prom-On, Santitham;

Publisher: Elsevier

Publication year: 2021

Start page: 7

End page: 11

Number of pages: 5

ISBN: 9781665428415

ISSN: 0928-4931

eISSN: 1873-0191

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117471810&doi=10.1109%2fIBDAP52511.2021.9552077&partnerID=40&md5=66ce85ef1e49de982e0e6b1eb4ab5cec

Languages: English-Great Britain (EN-GB)

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Abstract

This paper proposes to develop a model to detect the text semantic similarity in Thai by using a Siamese neural network with MaLSTM. As the text's intent is varied and hard to analyze, thus making the comparison between two sentences for the semantics similarity challenging. Our project wants to find similar intent of two questions by comparing the question between frequently asked questions (FAQs) and input questions from customers via Facebook Messenger of computer engineering at the King Mongkut's University of Technology Thonburi (CPE KMUTT). The data gather manually from the Pantip, CPE KMUTT FAQ, and Wikipedia, which create a dataset and data corpus. Although, the overall score performance of the model is not as we expected. Our model shows promising results, as it can detect between two questions' intent similarity. Results suggest that with the increase of the data, the model should have great potential for finding semantic similarity. © 2021 IEEE.

Keywords

Artificial Intelligence (AI), FAQ, intent similarity, sentence semantic similarity, Siamese Neural Network with MaLSTM