Unsupervised transactional query classification based on webpage form understanding

Query type classification aims to classify search queries into categories like navigational, informational and transactional, etc., according to the type of information need behind the queries. Although this problem has drawn many research attentions, previous methods usually require editors to label queries as training data or need domain knowledge to edit rules for predicting query type. Also, the existing work has been mainly focusing on the classification of informational and navigational query types. Transactional query classification has not been well addressed. In this work, we propose an unsupervised approach for transactional query classification. This method is based on the observation that, after the transactional queries are issued to a search engine, many users will click the search result pages and then have interactions with Web forms on these pages. The interactions, e.g., typing in text box, making selections from dropdown list, clicking on a button to execute actions, are used to specify detailed information of the transaction. By mining toolbar search log data, which records the associations between queries and Web forms clicked by users, we can get a set of good quality transactional queries without using manual labeling efforts. By matching these automatically acquired transactional queries and their associated Web form contents, we can generalize these queries into patterns. These patterns can be used to classify queries which are not covered by search log. Our experiments indicate that transactional queries produced by this method have good quality. The pattern based classifier achieves 83 % F1 classification result. This is very effective considering the fact that we do not adopt any labeling efforts to train the classifier

Longitudinal analysis of search engine query logs - temporal coverage

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical references leaves 53-60.The internet is growing day-by-day and the usage of web search engines is continuously increasing. Main page of browsers started by internet users is typically the home page of a search engine. To navigate a certain web site, most of the people prefer to type web sites’ name to search engine interface instead of using internet browsers’ address bar. Considering this important role of search engines as the main entry point to the web, we need to understand Web searching trends that are emerging over time. We believe that temporal analysis of returned query results by search engines reveals important insights for the current situation and future directions of web searching. In this thesis, we provide a large-scale analysis of the evolution of query results obtained from a real search engine at two distant points in time, namely, in 2007 and 2010, for a set of 630000 real queries. Our analyses in this work attempt to find answers to several critical questions regarding the evolution of Web search results. We believe that this work, being a large-scale longitudinal analysis of query results, would shed some light on those questions.Yılmaz, OğuzM.S