mlsanityData to 7 Oct 2026

Predicting the Semantic Textual Similarity with Siamese CNN and LSTM

arXiv 1810.10641 ↗First posted 24 Oct 2018cs.CLcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Oct 2018.

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Citing papers per year: 2021: 3; 2022: 6; 2023: 4; 2024: 0; 2025: 0; 2026: 1.

14 papers.

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