Paper: A Graph-based Semi-Supervised Learning for Question-Answering

ACL ID P09-1081
Title A Graph-based Semi-Supervised Learning for Question-Answering
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009

We present a graph-based semi-supervised learning for the question-answering (QA) task for ranking candidate sentences. Us- ing textual entailment analysis, we obtain entailment scores between a natural lan- guage question posed by the user and the candidate sentences returned from search engine. The textual entailment between two sentences is assessed via features rep- resenting high-level attributes of the en- tailment problem such as sentence struc- ture matching, question-type named-entity matching based on a question-classifier, etc. We implement a semi-supervised learning (SSL) approach to demonstrate that utilization of more unlabeled data points can improve the answer-ranking task of QA. We create a graph for labeled and unlabeled data using match-scores of textual entailment featu...