Probabilistic query expansion using query logs
Proceedings of the 11th international conference on World Wide Web
Discovery of inference rules for question-answering
Natural Language Engineering
Learning surface text patterns for a Question Answering system
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Question answering passage retrieval using dependency relations
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Building a reusable test collection for question answering
Journal of the American Society for Information Science and Technology - Research Articles
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
Open-domain question: answering
Foundations and Trends in Information Retrieval
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Quasi-synchronous grammars: alignment by soft projection of syntactic dependencies
StatMT '06 Proceedings of the Workshop on Statistical Machine Translation
Learning of graph-based question answering rules
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
Query rewriting using monolingual statistical machine translation
Computational Linguistics
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In Information Retrieval (IR) in general and Question Answering (QA) in particular, queries and relevant textual content often significantly differ in their properties and are therefore difficult to relate with traditional IR methods, e.g. key-word matching. In this paper we describe an algorithm that addresses this problem, but rather than looking at it on a term matching/term reformulation level, we focus on the syntactic differences between questions and relevant text passages. To this end we propose a novel algorithm that analyzes dependency structures of queries and known relevant text passages and acquires transformational patterns that can be used to retrieve relevant textual content. We evaluate our algorithm in a QA setting, and show that it outperforms a baseline that uses only dependency information contained in the questions by 300% and that it also improves performance of a state of the art QA system significantly.