dc.contributor.authorTripathi Surabhita
dc.date.accessioned2014-04-25T02:54:29Z
dc.date.available2014-04-25T02:54:29Z
dc.date.copyright2014en_US
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10356/59189
dc.description.abstractIn this report, the semantic information in parse selection is analyzed. A Python software model was used to carry out feature engineering on semantic parsing results by parsers. The data used was from the SemCor corpus and WeScience corpus. Different types of semantic features were generated using the model and training and testing was conducted using a maximum entropy model TADM. Error analysis was performed on the entire SemCor and WeScience corpus by reproducing the old results. Generalized features provide better parse selection accuracy than more specific features. Further Machine learning was performed using ELM, Extreme Machine Learning technique, to compare the parse ranking results with TADM. The key fundamental task is to understand the meaning of a word in a sentence and semantic relations between words, resolving ambiguities by considering context.en_US
dc.format.extent81 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineeringen_US
dc.titleMeaning representation in natural language processingen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Engineering)en_US
dc.contributor.supervisor2Kim Jung-Jaeen_US
dc.contributor.supervisor2Francis Bonden_US


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