Development of semantic feature engineering for statistical analysis on parse ranking
Date of Issue2013
School of Computer Engineering
Centre for Advanced Information Systems
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.
DRNTU::Engineering::Computer science and engineering::Information systems::Information storage and retrieval
Final Year Project (FYP)
Nanyang Technological University