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ACM SIGIR Forum
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Automatic identification of user goals in Web search
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Sentiment in short strength detection informal text
Journal of the American Society for Information Science and Technology
A text mining approach for definition question answering
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
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ACM Transactions on the Web (TWEB)
Information Sciences: an International Journal
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We investigate the problem of mining "tips" from Yahoo! Answers and displaying those tips in response to related web queries. Here, a "tip" is a short, concrete and self-contained bit of non-obvious advice such as "To zest a lime if you don't have a zester : use a cheese grater." First, we estimate the volume of web queries with "how-to" intent, which could be potentially addressed by a tip. Second, we analyze how to detect such queries automatically without solely relying on literal "how to *" patterns. Third, we describe how to derive potential tips automatically from Yahoo! Answers, and we develop machine-learning techniques to remove low-quality tips. Finally, we discuss how to match web queries with "how-to" intent to tips. We evaluate both the quality of these direct displays as well as the size of the query volume that can be addressed by serving tips.