@inproceedings{f3dcbc200383421c849a2cc8139c19de,
title = "Measuring Semantic Similarity between Sentences Using A Siamese Neural Network",
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.",
keywords = "GRU, metric learning, Neural networks, recurrent neural network, semantic analysis, siamese neural networks, word embedding",
author = "Ichida, {Alexandre Yukio} and Felipe Meneguzzi and Ruiz, {Duncan D.}",
note = "Funding Information: Felipe thanks CNPq for partial financial support under its PQ fellowship, grant number 305969/2016-1. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Joint Conference on Neural Networks, IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",
year = "2018",
month = oct,
day = "10",
doi = "10.1109/IJCNN.2018.8489433",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings",
}