Abstract
Measuring the semantic similarity of sentence pairs is an important natural language processing (NLP) problem and has many applications in many NLP systems. Sentence similarity is used to improve the performance of many systems such as machine translation, speech recognition, automatic question and answer, text summarization. However, accurately evaluate the semantic similarity between sentences is still a challenge. Up to now, there are not sentence similarity methods, which exploit Vietnamese specific characteristics, have been proposed. Moreover, there are not sentence similarity datasets for Vietnamese that have been published. In this paper, we propose a new method to measure the semantic similarity of Vietnamese sentence pairs based on combining lexical similarity score and distribution semantic similarity score of two sentences. The experimental results have shown that our proposed model has high performance for the Vietnamese semantic similarity problem.
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Notes
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In this study, we use google API to translate Chinese, Laotian, Khmer sentences into Vietnamese.
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Acknowledgments
This paper is part of project number KC-4.0-12/19-25 that is led by Doctor Nguyen Van Vinh and funded by the Science and Technology Program KC 4.0.
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Bui, VT., Nguyen, PT. (2022). Measuring Semantic Similarity of Vietnamese Sentences Based on Lexical and Distribution Similarity. In: Le Thi, H.A., Pham Dinh, T., Le, H.M. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. MCO 2021. Lecture Notes in Networks and Systems, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-030-92666-3_22
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