Semantic Textual Similarity with Siamese Neural Networks

2019-09-01 · RANLP 2019 9 · Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov

Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing methods

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Information Retrieval Question Answering Retrieval Semantic Textual SimilaritySTS

Predicting the Semantic Textual Similarity with Siamese CNN and LSTM

2018-10-24 · JEPTALNRECITAL 2018 5 · Elvys Linhares Pontes, Stéphane Huet, Andréa Carneiro Linhares, Juan-Manuel Torres-Moreno

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 …

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Tree kernels (TKs) and neural networks are two effective approaches for automatic feature engineering. In this paper, we combine them by modeling context word similarity in semantic TKs. This way, the latter can operate …

Feature Engineering Question Answering Relation Extraction Semantic Similarity+5

A Siamese CNN Architecture for Learning Chinese Sentence Similarity

2020-12-01 · Asian Chapter of the Association for Computational Linguistics 2020 · Haoxiang Shi, Cen Wang, Tetsuya Sakai

This paper presents a deep neural architecture which applies the siamese convolutional neural network sharing model parameters for learning a semantic similarity metric between two sentences. In addition, two different s…

Extractive Summarization for Arabic Documents Using SAraBERT with a Semantic Siamese Similarity Evaluation Metric

2026-08-21 · Sami Shames El Deen, Mariette Awad arxiv

In this research, we introduce SAraBERT, an enhanced version of AraBERT which proposes inter-sentence transformer layers for extractive summarization tasks. To ensure that the summaries generated by SAraBERT achieve a hi…