NDLI: What Users Ask a Search Engine: Analyzing One Billion Russian Question Queries

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What Users Ask a Search Engine: Analyzing One Billion Russian Question Queries

Content Provider	ACM Digital Library
Author	Lezina, Galina Braslavski, Pavel Stein, Benno Völske, Michael Hagen, Matthias
Abstract	We analyze the question queries submitted to a large commercial web search engine to get insights about what people ask, and to better tailor the search results to the users' needs. Based on a dataset of about one billion question queries submitted during the year 2012, we investigate askers' querying behavior with the support of automatic query categorization. While the importance of question queries is likely to increase, at present they only make up 3-4% of the total search traffic. Since questions are such a small part of the query stream, and are more likely to be unique than shorter queries, click-through information is typically rather sparse. Thus, query categorization methods based on the categories of clicked web documents do not work well for questions. As an alternative, we propose a robust question query classification method that uses the labeled questions from a large community question answering platform (CQA) as a training set. The resulting classifier is then transferred to the web search questions. Even though questions on CQA platforms tend to be different to web search questions, our categorization method proves competitive with strong baselines with respect to classification accuracy. To show the scalability of our proposed method we apply the classifiers to about one billion question queries and discuss the trade-offs between performance and accuracy that different classification models offer.
Starting Page	1571
Ending Page	1580
Page Count	10
File Format	PDF
ISBN	9781450337946
DOI	10.1145/2806416.2806457
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-10-17
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Query classification Question queries Query log analysis Community question answering (cqa)
Content Type	Text
Resource Type	Article

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