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.
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页数：7
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