# Predicting the Semantic Textual Similarity with Siamese CNN and LSTM

arXiv: https://arxiv.org/abs/1810.10641 · HTML: https://mlsanity.com/paper/1810.10641/

## Key facts

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

## Built on by (most cited first, 14 of 14)

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- [Semantic Similarity Computing for Scientific Academic Conferences fused with domain features](https://mlsanity.com/paper/2203.12593/) (2022)
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- [JU_NLP at HinglishEval: Quality Evaluation of the Low-Resource Code-Mixed Hinglish Text](https://mlsanity.com/paper/2206.08053/) (2022)
- [A Novel Plagiarism Detection Approach Combining BERT-based Word Embedding, Attention-based LSTMs and an Improved Differential Evolution Algorithm](https://mlsanity.com/paper/2305.02374/) (2023)

## 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.