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http://elar.urfu.ru/handle/10995/102282
Название: | What users ask a search engine: Analyzing one billion Russian question queries |
Авторы: | Völske, M. Braslavski, P. Hagen, M. Lezina, G. Stein, B. |
Дата публикации: | 2015 |
Издатель: | Association for Computing Machinery |
Библиографическое описание: | What users ask a search engine: Analyzing one billion Russian question queries / M. Völske, P. Braslavski, M. Hagen, et al. — DOI 10.1145/2806416.2806457 // International Conference on Information and Knowledge Management, Proceedings. — 2015. — Vol. 19-23-Oct-2015. — P. 1571-1580. |
Аннотация: | 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. Our findings reveal what people ask a search engine and also how this contrasts behavior on a CQA platform. |
Ключевые слова: | COMMUNITY QUESTION ANSWERING (CQA) QUERY CLASSIFICATION QUERY LOG ANALYSIS QUESTION QUERIES CLASSIFICATION (OF INFORMATION) ECONOMIC AND SOCIAL EFFECTS INFORMATION RETRIEVAL KNOWLEDGE MANAGEMENT ONLINE SEARCHING SEARCH ENGINES WEBSITES CATEGORIZATION METHODS CLASSIFICATION ACCURACY CLASSIFICATION MODELS COMMUNITY QUESTION ANSWERING QUERY CLASSIFICATION QUERY LOG ANALYSIS QUESTION QUERIES SEARCH TRAFFICS WORLD WIDE WEB |
URI: | http://elar.urfu.ru/handle/10995/102282 |
Условия доступа: | info:eu-repo/semantics/openAccess |
Идентификатор SCOPUS: | 84959268782 |
Идентификатор PURE: | 698938 df2bce53-cfce-4b8d-92c5-1605e290cca2 |
ISBN: | 9781450337946 |
DOI: | 10.1145/2806416.2806457 |
Располагается в коллекциях: | Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC |
Файлы этого ресурса:
Файл | Описание | Размер | Формат | |
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2-s2.0-84959268782.pdf | 272,68 kB | Adobe PDF | Просмотреть/Открыть |
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