Improved answer ranking in social question-answering portals
Proceedings of the 3rd international workshop on Search and mining user-generated contents
Exploiting user profile information for answer ranking in cQA
Proceedings of the 21st international conference companion on World Wide Web
Exploiting semantic roles for asynchronous question answering in an educational setting
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Can click patterns across user's query logs predict answers to definition questions?
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Full machine translation for factoid question answering
EACL 2012 Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra)
Why question answering using sentiment analysis and word classes
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Learning to rank for robust question answering
Proceedings of the 21st ACM international conference on Information and knowledge management
Collaboratively built semi-structured content and Artificial Intelligence: The story so far
Artificial Intelligence
Semantic models for answer re-ranking in question answering
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Evolutionary optimization for ranking how-to questions based on user-generated contents
Expert Systems with Applications: An International Journal
Learning regular expressions to template-based FAQ retrieval systems
Knowledge-Based Systems
Information Sciences: an International Journal
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This work investigates the use of linguistically motivated features to improve search, in particular for ranking answers to non-factoid questions. We show that it is possible to exploit existing large collections of question-answer pairs (from online social Question Answering sites) to extract such features and train ranking models which combine them effectively. We investigate a wide range of feature types, some exploiting natural language processing such as coarse word sense disambiguation, named-entity identification, syntactic parsing, and semantic role labeling. Our experiments demonstrate that linguistic features, in combination, yield considerable improvements in accuracy. Depending on the system settings we measure relative improvements of 14% to 21% in Mean Reciprocal Rank and Precision@1, providing one of the most compelling evidence to date that complex linguistic features such as word senses and semantic roles can have a significant impact on large-scale information retrieval tasks.