Measuring Semantic Similarity Between Sentences Using A Siamese Neural Network

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 s...

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Bibliographic Details
Main Authors: Ichida, Alexandre Yukio, Meneguzzi, Felipe, Ruiz, Duncan D.
Format: Conference Proceeding
Language:English
Subjects:
Computer architecture
GRU
Indexes
Measurement
metric learning
Neural networks
recurrent neural network
Recurrent neural networks
semantic analysis
Semantics
siamese neural networks
Training
word embedding
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Description
Summary: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.
ISSN:2161-4407
DOI:10.1109/IJCNN.2018.8489433