Measuring Semantic Similarity Between Sentences Using a Siamese Neural Network

被引：0

作者：

Ichida, Alexandre Yukio ^{[1
]}

Meneguzzi, Felipe ^{[1
]}

Ruiz, Duncan D. ^{[1
]}

机构：

[1] Pontificia Univ Catolica Rio Grande do Sul, Porto Alegre, RS, Brazil

来源：

2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2018年

关键词：

Neural networks; word embedding; recurrent neural network; GRU; metric learning; siamese neural networks; semantic analysis;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页数：7

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