Modern Information Retrieval
ACM SIGIR Forum
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Automatic identification of user goals in Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
Ranking definitions with supervised learning methods
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Probabilistic model for definitional question answering
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Reranking answers for definitional QA using language modeling
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Learning to identify single-snippet answers to definition questions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Fast generation of result snippets in web search
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Document Compaction for Efficient Query Biased Snippet Generation
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Global ranking by exploiting user clicks
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Mining and re-ranking for answering biographical queries on the web
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Extraction of definitions using grammar-enhanced machine learning
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Improving quality of training data for learning to rank using click-through data
Proceedings of the third ACM international conference on Web search and data mining
Inferring query intent from reformulations and clicks
Proceedings of the 19th international conference on World wide web
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Learning word-class lattices for definition and hypernym extraction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Web search solved?: all result rankings the same?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Learning to rank answers to non-factoid questions from web collections
Computational Linguistics
Answering definition questions using web knowledge bases
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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In this paper, we examined click patterns produced by users of Yahoo! search engine when prompting definition questions. Regularities across these click patterns are then utilized for constructing a large and heterogeneous training corpus for answer ranking. In a nutshell, answers are extracted from clicked web-snippets originating from any class of web-site, including Knowledge Bases (KBs). On the other hand, nonanswers are acquired from redundant pieces of text across web-snippets. The effectiveness of this corpus was assessed via training two state-of-the-art models, wherewith answers to unseen queries were distinguished. These testing queries were also submitted by search engine users, and their answer candidates were taken from their respective returned web-snippets. This corpus helped both techniques to finish with an accuracy higher than 70%, and to predict over 85% of the answers clicked by users. In particular, our results underline the importance of non-KB training data.