Answers, not links: extracting tips from yahoo! answers to address how-to web queries

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.

[1]  Ellen M Voorhees.  Question answering in TREC , 2001, CIKM '01.

[2]  Eugene Agichtein,et al.  Finding the right facts in the crowd: factoid question answering over social media , 2008, WWW.

[3]  Sanghee Oh,et al.  Users' relevance criteria for evaluating answers in a social Q&A site , 2009, J. Assoc. Inf. Sci. Technol..

[4]  Ethem Alpaydin,et al.  Introduction to machine learning , 2004, Adaptive computation and machine learning.

[5]  Michael Schroeder,et al.  Facts from text: can text mining help to scale-up high-quality manual curation of gene products with ontologies? , 2008, Briefings Bioinform..

[6]  Gilad Mishne,et al.  Finding high-quality content in social media , 2008, WSDM '08.

[7]  Mihai Surdeanu,et al.  Learning to Rank Answers on Large Online QA Collections , 2008, ACL.

[8]  Filip Radlinski,et al.  Inferring query intent from reformulations and clicks , 2010, WWW '10.

[9]  Patrice Bellot,et al.  Overview of the 2009 QA Track: Towards a Common Task for QA, Focused IR and Automatic Summarization Systems , 2009, INEX.

[10]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[11]  William R. Hersh,et al.  A survey of current work in biomedical text mining , 2005, Briefings Bioinform..

[12]  F. Maxwell Harper,et al.  Facts or friends?: distinguishing informational and conversational questions in social Q&A sites , 2009, CHI.

[13]  Lada A. Adamic,et al.  Knowledge sharing and yahoo answers: everyone knows something , 2008, WWW.

[14]  Manuel Montes-y-Gómez,et al.  A Text Mining Approach for Definition Question Answering , 2006, FinTAL.

[15]  Ioannis Antonellis,et al.  Simrank++: query rewriting through link analysis of the clickgraph (poster) , 2007, Proc. VLDB Endow..

[16]  Daniel E. Rose,et al.  Understanding user goals in web search , 2004, WWW '04.

[17]  Mike Thelwall,et al.  Sentiment in short strength detection informal text , 2010 .

[18]  Jimmy J. Lin,et al.  Quantitative evaluation of passage retrieval algorithms for question answering , 2003, SIGIR.

[19]  Shengrui Wang,et al.  Identifying authoritative actors in question-answering forums: the case of Yahoo! answers , 2008, KDD.

[20]  Gerhard Weikum,et al.  YAGO: A Large Ontology from Wikipedia and WordNet , 2008, J. Web Semant..

[21]  Tat-Seng Chua,et al.  Question answering passage retrieval using dependency relations , 2005, SIGIR '05.

[22]  Benjamin Rey,et al.  Generating query substitutions , 2006, WWW '06.

[23]  Zhenyu Liu,et al.  Automatic identification of user goals in Web search , 2005, WWW '05.

[24]  Andrei Broder,et al.  A taxonomy of web search , 2002, SIGF.

[25]  Berkant Barla Cambazoglu,et al.  Web search solved?: all result rankings the same? , 2010, CIKM '10.

[26]  Philipp Birken,et al.  Numerical Linear Algebra , 2011, Encyclopedia of Parallel Computing.

[27]  Amanda Spink,et al.  Determining the informational, navigational, and transactional intent of Web queries , 2008, Inf. Process. Manag..