WordNet: a lexical database for English
Communications of the ACM
Building a question answering test collection
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Exploiting redundancy in question answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Discovery of inference rules for question-answering
Natural Language Engineering
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Lexical chains for question answering
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Logic form transformation of WordNet and its applicability to question answering
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Machine Learning
New Directions in Question Answering
Information Retrieval
The importance of syntactic parsing and inference in semantic role labeling
Computational Linguistics
Scaling textual inference to the web
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Introduction to the CoNLL-2005 shared task: semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Markov Logic: An Interface Layer for Artificial Intelligence
Markov Logic: An Interface Layer for Artificial Intelligence
Learning first-order Horn clauses from web text
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Overview of ResPubliQA 2009: question answering evaluation over European legislation
CLEF'09 Proceedings of the 10th cross-language evaluation forum conference on Multilingual information access evaluation: text retrieval experiments
Tuffy: scaling up statistical inference in Markov logic networks using an RDBMS
Proceedings of the VLDB Endowment
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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Most open-domain question answering systems achieve better performances with large corpora, such as Web, by taking advantage of information redundancy. However, explicit answers are not always mentioned in the corpus, many answers are implicitly contained and can only be deducted by inference. In this paper, we propose an approach to discover logical knowledge for deep question answering, which automatically extracts knowledge in an unsupervised, domain-independent manner from background texts and reasons out implicit answers for the questions. Firstly, we use semantic role labeling to transform natural language expressions to predicates in first-order logic. Then we use association analysis to uncover the implicit relations among these predicates and build propositions for inference. Since our knowledge is drawn from different sources, we use Markov logic to merge multiple knowledge bases without resolving their inconsistencies. Our experiments show that these propositions can improve the performance of question answering significantly.