Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/52309
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dc.contributor.authorYin, Xiaocheng.
dc.date.accessioned2013-05-06T01:46:59Z
dc.date.available2013-05-06T01:46:59Z
dc.date.copyright2013en_US
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/10356/52309
dc.description.abstractIn this report, the use of semantic information in parse selection is investigated. It is shown that increasing sense-based semantic features based on deep linguistic processing directly helps improving the effectiveness of parse selection. A Python software model was implemented to carry out feature engineering on semantic parsing results by parsers and the data was from SemCor corpus. Different types of semantic features are generated using the model and training and testing was conducted using a maximum entropy model TADM. Also, baseline features are generalized upwards in the WordNet hierarchy to help investigate the effectiveness of disambiguation in parse selection. Generalized features provide better parse selection accuracy than more specific features.en_US
dc.format.extent71 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrievalen_US
dc.subjectDRNTU::Humanities::Linguistics::Semanticsen_US
dc.titleDevelopment of semantic feature engineering for statistical analysis on parse rankingen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorKoe Choon Chiaw, Lawrenceen_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Engineering)en_US
dc.contributor.researchCentre for Advanced Information Systemsen_US
dc.contributor.supervisor2Kim Jung-Jaeen_US
dc.contributor.supervisor2Francis Bonden_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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