dc.contributor.authorLi, Linjie
dc.date.accessioned2018-09-10T13:19:12Z
dc.date.available2018-09-10T13:19:12Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/10356/75959
dc.description.abstractRelation extraction is a very important research area in Natural Language Processing. This thesis mainly concentrate on identifying cause-effect relation which can be used in various fields like question answering and medical science. A relation classification system is built in the thesis to achieve the target. The whole system consists of two parts. The first one is text representation. An accurate text representation is key to the performance of the whole classification system. Two methods are used in this part: traditional Bag of Words and Word embedding. Different types of word embedding methods are also compared. The second part is classification, results of word embedding can be further used to extract features and do the classification based on Neural Networks. Two popular structures: Convolutional Neural Network and Long Short Time Memory are implemented and compared. Experiments show that using the combination of Word embedding and Neural Network based classification performs much better than using traditional Bag of words to represent text and do the classification directly. The distinguished performance of CNN in solving relation classification problems are shown by experiments. Some methods are also taken to improve the performance of CNN-based structure in order to achieve the best classification results.en_US
dc.format.extent77 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleRelation identification for reasoningen_US
dc.typeThesis
dc.contributor.supervisorMao Kezhien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Signal Processing)en_US


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