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Title: Development of semantic feature engineering for statistical analysis on parse ranking
Authors: Yin, Xiaocheng.
Keywords: DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval
Issue Date: 2013
Abstract: In 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.
Schools: School of Computer Engineering 
Research Centres: Centre for Advanced Information Systems 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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