Feature Combination for Measuring Sentence Similarity

• Main Author: Shareghi Nojehdeh, Ehsan
• Format: Others
• Published: 2013
• Online Access: http://spectrum.library.concordia.ca/977146/1/Shareghi%2DNojehdeh_MCompSc_S2013.pdf; Shareghi Nojehdeh, Ehsan <http://spectrum.library.concordia.ca/view/creators/Shareghi_Nojehdeh=3AEhsan=3A=3A.html> (2013) Feature Combination for Measuring Sentence Similarity. Masters thesis, Concordia University.

Sentence similarity is one of the core elements of Natural Language Processing (NLP) tasks such as Recognizing Textual Entailment, and Paraphrase Recognition. Over the years, different systems have been proposed to measure similarity between fragments of texts. In this research, we propose a new two phase supervised learning method which uses a combination of lexical features to train a model for predicting similarity between sentences. Each of these features, covers an aspect of the text on implicit or explicit level. The two phase method uses all combinations of the features in the feature space and trains separate models based on each combination. Then it creates a meta-feature space and trains a final model based on that. The thesis contrasts existing approaches that use feature selection, because it does not aim to find the best subset of the possible features. We show that this two step process significantly improves the results achieved by single-layer standard learning methodology, and achieves the level of performance that is comparable to the existing state-of-the-art methods.