Measuring Short Text Semantic Similarity with Deep Learning Models

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dc.contributor.advisorHuang, Xiangji
dc.creatorGe, Jun
dc.date.accessioned2018-11-21T13:41:26Z
dc.date.available2018-11-21T13:41:26Z
dc.date.copyright2018-05-15
dc.date.issued2018-11-21
dc.date.updated2018-11-21T13:41:26Z
dc.degree.disciplineInformation Systems and Technology
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractNatural 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.
dc.identifier.urihttp://hdl.handle.net/10315/35482
dc.language.isoen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectInformation technology
dc.subject.keywordsText similarity
dc.subject.keywordsDeep learning models
dc.subject.keywordsNatural language processing
dc.titleMeasuring Short Text Semantic Similarity with Deep Learning Models
dc.typeElectronic Thesis or Dissertation

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