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2009

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

9 years 1 months ago
A Graph-based Semi-Supervised Learning for Question-Answering
We present a graph-based semi-supervised learning for the question-answering (QA) task for ranking candidate sentences. Using textual entailment analysis, we obtain entailment scores between a natural language question posed by the user and the candidate sentences returned from search engine. The textual entailment between two sentences is assessed via features representing high-level attributes of the entailment problem such as sentence structure 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 features as similarity weights between data points. We apply a summarization method on the graph to make the computations feasible on large datasets. With a new representation of graph-based SSL on QA datasets usin...
Asli Çelikyilmaz, Marcus Thint, Zhiheng Hua
Added 16 Feb 2011
Updated 16 Feb 2011
Type Journal
Year 2009
Where ACL
Authors Asli Çelikyilmaz, Marcus Thint, Zhiheng Huang
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