Discovery of inference rules for question-answering
Natural Language Engineering
Open information extraction from the web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Boeing's NLP system and the challenges of semantic representation
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
Coupled semi-supervised learning for information extraction
Proceedings of the third ACM international conference on Web search and data mining
Global learning of typed entailment rules
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Reranking bilingually extracted paraphrases using monolingual distributional similarity
GEMS '11 Proceedings of the GEMS 2011 Workshop on GEometrical Models of Natural Language Semantics
Identifying relations for open information extraction
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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As part of our work on building a "knowledgeable textbook" about biology, we are developing a textual question-answering (QA) system that can answer certain classes of biology questions posed by users. In support of that, we are building a "textual KB" - an assembled set of semi-structured assertions based on the book - that can be used to answer users' queries, can be improved using global consistency constraints, and can be potentially validated and corrected by domain experts. Our approach is to view the KB as systematically caching answers from a QA system, and the QA system as assembling answers from the KB, the whole process kickstarted with an initial set of textual extractions from the book text itself. Although this research is only in a preliminary stage, we summarize our progress and lessons learned to date.