Abstract
The task of measure semantic redundancy between sentences demands a thorough interpretation from the reader because phrase meaning may be ambiguous. Detecting semantic similarity is a difficult problem because natural language, besides ambiguity, offers almost infinite possibilities to express the same idea. This paper adapts a siamese neural network architecture trained to measure the semantic similarity between two sentences through metric learning. The resulting solution should help in writing more efficient and informative text.
Original language | English |
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Title of host publication | 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781509060146 |
DOIs | |
Publication status | Published - 10 Oct 2018 |
Event | 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018 |
Publication series
Name | Proceedings of the International Joint Conference on Neural Networks |
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Volume | 2018-July |
Conference
Conference | 2018 International Joint Conference on Neural Networks, IJCNN 2018 |
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Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 8/07/18 → 13/07/18 |
Bibliographical note
Funding Information:Felipe thanks CNPq for partial financial support under its PQ fellowship, grant number 305969/2016-1.
Publisher Copyright:
© 2018 IEEE.
Keywords
- GRU
- metric learning
- Neural networks
- recurrent neural network
- semantic analysis
- siamese neural networks
- word embedding