He, H., Gimpel, K. and Lin, J. (2015) Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, 17-21 September 2015, 1576-1586. - References

Browse Menu >>

Browse Subjects >>

Computer Science & Communications

Earth & Environmental Sciences

Social Sciences & Humanities

Why Us? >>

- Open Access
- Peer-reviewed
- Rapid publication
- Lifetime hosting
- Free indexing service
- Free promotion service
- More citations
- Search engine friendly

Free SCIRP Newsletters>>

Add your e-mail address to receive free newsletters from SCIRP.

Contact Us >>

customer@scirp.org

+86 18163351462(WhatsApp)

1655362766

Paper Publishing WeChat

Book Publishing WeChat

(or Email:book@scirp.org)

Article citations
More>>

He, H., Gimpel, K. and Lin, J. (2015) Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, 17-21 September 2015, 1576-1586.
https://doi.org/10.18653/v1/D15-1181

has been cited by the following article:

TITLE: A Sentence Similarity Estimation Method Based on Improved Siamese Network

AUTHORS: Ziming Chi, Bingyan Zhang

KEYWORDS: Sentence Similarity, Sentence Modeling, Similarity Measurement, Attention Mechanism, Fully-Connected Layer, Disorder Sentence Dataset

JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.10 No.4, October 24, 2018

ABSTRACT: In this paper we employ an improved Siamese neural network to assess the semantic similarity between sentences. Our model implements the function of inputting two sentences to obtain the similarity score. We design our model based on the Siamese network using deep Long Short-Term Memory (LSTM) Network. And we add the special attention mechanism to let the model give different words different attention while modeling sentences. The fully-connected layer is proposed to measure the complex sentence representations. Our results show that the accuracy is better than the baseline in 2016. Furthermore, it is showed that the model has the ability to model the sequence order, distribute reasonable attention and extract meanings of a sentence in different dimensions.