Measuring Semantic Similarity between Sentences Using A Siamese Neural Network

Alexandre Yukio Ichida, Felipe Meneguzzi, Duncan D. Ruiz

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

21 Citations (Scopus)

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 languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509060146
DOIs
Publication statusPublished - 10 Oct 2018
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2018-July

Conference

Conference2018 International Joint Conference on Neural Networks, IJCNN 2018
Country/TerritoryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

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

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