The nature of statistical learning theory
The nature of statistical learning theory
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
WordNet: a lexical database for English
HLT '94 Proceedings of the workshop on Human Language Technology
Graph-based Semi-supervised Learning Algorithm for Web Page Classification
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Word sense disambiguation using label propagation based semi-supervised learning
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Relation extraction using label propagation based semi-supervised learning
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Exploring correlation of dependency relation paths for answer extraction
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Methods for using textual entailment in open-domain question answering
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Biased LexRank: Passage retrieval using random walks with question-based priors
Information Processing and Management: an International Journal
Question classification using head words and their hypernyms
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Semi-supervised Gaussian process classifiers
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using semi-supervised learning for question classification
ICCPOL'06 Proceedings of the 21st international conference on Computer Processing of Oriental Languages: beyond the orient: the research challenges ahead
Experiments in passage selection and answer identification for question answering
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
LDA based similarity modeling for question answering
SS '10 Proceedings of the NAACL HLT 2010 Workshop on Semantic Search
Semi-supervised truth discovery
Proceedings of the 20th international conference on World wide web
Semi-automatically extracting FAQs to improve accessibility of software development knowledge
Proceedings of the 34th International Conference on Software Engineering
A phased ranking model for question answering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Robust predictive model for evaluating breast cancer survivability
Engineering Applications of Artificial Intelligence
Sharpened graph ensemble for semi-supervised learning
Intelligent Data Analysis
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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 using only a handful of features, and under limited amounts of labeled data, we show improvement in generalization performance over state-of-the-art QA models.