Predicting the Semantic Textual Similarity with Siamese CNN and LSTM

Semantic Textual Similarity (STS) is the basis of many applications in Natural Language Processing (NLP). Our system combines convolution and recurrent neural networks to measure the semantic similarity of sentences. It uses a convolution network to take account of the local context of words and an...

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Bibliographic Details
Published in:arXiv.org 2018-10
Main Authors: Elvys Linhares Pontes, Huet, Stéphane, Andréa Carneiro Linhares, Torres-Moreno, Juan-Manuel
Format: Article
Language:English
Subjects:
Convolution
Natural language processing
Recurrent neural networks
Semantics
Sentences
Similarity
Online Access:Get full text
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Summary:Semantic Textual Similarity (STS) is the basis of many applications in Natural Language Processing (NLP). Our system combines convolution and recurrent neural networks to measure the semantic similarity of sentences. It uses a convolution network to take account of the local context of words and an LSTM to consider the global context of sentences. This combination of networks helps to preserve the relevant information of sentences and improves the calculation of the similarity between sentences. Our model has achieved good results and is competitive with the best state-of-the-art systems.
ISSN:2331-8422