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Measuring Short Text Semantic Similarity with Deep Learning Models

Measuring Short Text Semantic Similarity with Deep Learning Models

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Title: Measuring Short Text Semantic Similarity with Deep Learning Models
Author: Ge, Jun
Abstract: Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken, which is a subfield of artificial intelligence (AI). The development of NLP applications is challenging because computers traditionally require humans to speak" to them in a programming language that is precise, unambiguous and highly structured, or through a limited number of clearly enunciated voice commands. We study the use of deep learning models, the state-of-the-art artificial intelligence (AI) method, for the problem of measuring short text semantic similarity in NLP area. In particular, we propose a novel deep neural network architecture to identify semantic similarity for pairs of question sentence. In the proposed network, multiple channels of knowledge for pairs of question text can be utilized to improve the representation of text. Then a dense layer is used to learn a classifier for classifying duplicated question pairs. Through extensive experiments on the Quora test collection, our proposed approach has shown remarkable and significant improvement over strong baselines, which verifies the effectiveness of the deep models as well as the proposed deep multi-channel framework.
Subject: Information technology
Keywords: Text similarity
Deep learning models
Natural language processing
Type: Electronic Thesis or Dissertation
Rights: Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
URI: http://hdl.handle.net/10315/35482
Supervisor: Huang, Xiangji
Degree: MA - Master of Arts
Program: Information Systems and Technology
Exam date: 2018-05-15
Publish on: 2018-11-21

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