Automatic web query classification using labeled and unlabeled training data

Beitzel, Steven M.; Jensen, Eric C.; Frieder, Ophir; Grossman, David A.; Lewis, David; Chowdhury, Abdur; Kolcz, Aleksandr

doi:10.1145/1076034.1076138

Cited by 64 publications

(43 citation statements)

References 4 publications

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“…Typical query understanding operations include refinements of the original query (Huang & Efthimiadis, 2009), such as spelling correction (Ahmad & Kondrak, 2005;Li et al, 2006), acronym expansion (Jain et al, 2007), stemming (Porter, 1980;Peng et al, 2007a) deletion (Kumaran & Allan, 2008;Kumaran & Carvalho, 2009), query segmentation (Risvik et al, 2003;Bergsma & Wang, 2007), and named entity recognition (Guo et al, 2009). Other common query understanding operations are query topic classification, aimed to restrict the scope of the retrieved documents (Beitzel et al, 2005;Shen et al, 2006), and query expansion, aimed to enhance the query representation with useful terms from the local corpus (Rocchio, 1971;Lavrenko & Croft, 2001;Carpineto & Romano, 2012), or from external resources, such as a query log (Cui et al, 2002) or a knowledge base such as Wikipedia (He & Ounis, 2007;Li et al, 2007;Xu et al, 2009).…”

Section: Query Processingmentioning

confidence: 99%

Explicit web search result diversification

Santos

2012

SIGIR Forum

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Queries submitted to a web search engine are typically short and often ambiguous. With the enormous size of the Web, a misunderstanding of the information need underlying an ambiguous query can misguide the search engine, ultimately leading the user to abandon the originally submitted query. In order to overcome this problem, a sensible approach is to diversify the documents retrieved for the user's query. As a result, the likelihood that at least one of these documents will satisfy the user's actual information need is increased. In this thesis, we argue that an ambiguous query should be seen as representing not one, but multiple information needs. Based upon this premise, we propose xQuAD---Explicit Query Aspect Diversification, a novel probabilistic framework for search result diversification. In particular, the xQuAD framework naturally models several dimensions of the search result diversification problem in a principled yet practical manner. To this end, the framework represents the possible information needs underlying a query as a set of keyword-based sub-queries. Moreover, xQuAD accounts for the overall coverage of each retrieved document with respect to the identified sub-queries, so as to rank highly diverse documents first. In addition, it accounts for how well each sub-query is covered by the other retrieved documents, so as to promote novelty--and hence penalise redundancy---in the ranking. The framework also models the importance of each of the identified sub-queries, so as to appropriately cater for the interests of the user population when diversifying the retrieved documents. Finally, since not all queries are equally ambiguous, the xQuAD framework caters for the ambiguity level of different queries, so as to appropriately trade-off relevance for diversity on a per-query basis. The xQuAD framework is general and can be used to instantiate several diversification models, including the most prominent models described in the literature. In particular, within xQuAD, each of the aforementioned dimensions of the search result diversification problem can be tackled in a variety of ways. In this thesis, as additional contributions besides the xQuAD framework, we introduce novel machine learning approaches for addressing each of these dimensions. These include a learning to rank approach for identifying effective subqueries as query suggestions mined from a query log, an intent-aware approach for choosing the ranking models most likely to be effective for estimating the coverage and novelty of multiple documents with respect to a sub-query, and a selective approach for automatically predicting how much to diversify the documents retrieved for each individual query. In addition, we perform the first empirical analysis of the role of novelty as a diversification strategy for web search. As demonstrated throughout this thesis, the principles underlying the xQuAD framework are general, sound, and effective. In particular, to validate the contributions of this thesis, we thoroughly assess the effectiveness of xQuAD under the standard experimentation paradigm provided by the diversity task of the TREC 2009, 2010, and 2011 Web tracks. The results of this investigation demonstrate the effectiveness of our proposed framework. Indeed, xQuAD attains consistent and significant improvements in comparison to the most effective diversification approaches in the literature, and across a range of experimental conditions, comprising multiple input rankings, multiple sub-query generation and coverage estimation mechanisms, as well as queries with multiple levels of ambiguity. Altogether, these results corroborate the state-of-the-art diversification performance of xQuAD. Available online at http://theses.gla.ac.uk/4106/.

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Section: Query Processingmentioning

confidence: 99%

Explicit web search result diversification

Santos

2012

SIGIR Forum

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“…Web user queries are classified into target taxonomy by Shen et al Some of similar wok has been done by Gravano et al [20] and Beitzel et al [21]. All their work is on classification of queries but the major difference between our approach and others is that the trained data is used for categorization of the result required.…”

Section: Related Workmentioning

confidence: 99%

Web Supported Query Taxonomy Classifier

AbdusSalamQureshi¹,

Jamal²

2012

IJCA

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A new approach called Web Supported Query Taxonomy Classifier is introduced in this paper, which generates better searching results. We combine WSQ, Web Supported Queries approach with Query Classification in which bridging classifier and category selection method is used for classification of queries. In this paper, Query categorization will build bridging classifier in an offline mode at mediator taxonomy integrated with the category selection method for the effectiveness and efficiency of online classification and then classifier is used for mapping input queries to target taxonomy in online mode. After this the queries are settled in virtual tables presented by WSQ tuples for the generation of web search result calling one or more search engines for improved results. Further a technique called asynchronous iteration is used for concurrency among multiple web search requests and query processing. KeywordsWeb Query Classification, Asynchronous Iteration, Bridging Classifier, Category Selection Method, WSQ, Web Supported Query Taxonomy Classifier. INTRODUCTIONNowadays information is available on single click. Internet acts as a tool which contains immense information. Web users input their required data and search engines search those queries. A number of processing phases are included in a very basic search engine, it includes, Indexing, Crawling, Query Processing, and Ranking. Indexing: to build an index to facilitate query processing, crawling: to discover the web pages on the internet, Query Processing: To Extract (based on user's query terms) the most relevant page and Ranking: Based on relevancy order the result.Real time information is maintained by the search engines based on algorithm running on a web crawler. A web crawler is an agenda for computer searching. It's a computer program that browse the world wide web in an automated or systematic way. Many search engines use web crawling as a way of providing up-to-date data. A copy of all the visited pages is created for later processing by a search engine that will make a directory for the downloaded pages to provide fast searches.Search engines have gained popularity over the World Wide Web. Information moves between the World Wide Web which is considered un-structured data and database which is structured data. Figure 1 shows the relationship of structured and unstructured data which provides vast amount of information. Figure 1 Information SplitSignificant deficiencies with respect to flexibility, precision and robustness are found over web search techniques. They take queries as input because these user queries are important medium by which a system can understand user's interest. These queries are assorted and significant which requires classifying them into small commercial taxonomy. They are short so they are vague; therefore they lack precise semantic description.Query classification is an application for giving better search pages result and WSQ arrange and manage data between structured and unstructured information available on intern...

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“…The taxonomy based data marts will help to access the contents of data warehouse and use analysis tools. [21] Information processing is one of the applications of data warehouse which is based on queries. These queries are categorized with the use of Query classifier and easily answerable to queries that reflect information stored in database directly.…”

Section: Applicability Of Multilayered Architecturementioning

confidence: 99%

Taxonomy based Data Marts

Qureshi¹,

Jamal²

2012

IJCA

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The aim of this paper is to depict new approach called taxonomy based data marts which add a new layer for the categorization of the queries using data warehouse which is the database that contains data relevant to an organization information and respond quickly to multi dimensional analytical queries. The new algorithm is introduced here for more precise results and time saving consumption using data marts which collects data for specific set of users or knowledge workers. Data warehouses often adopt a three-tier architecture. The bottom tier is a warehouse database server, which is typically a relational database system. The middle tier is an OLAP server, and the top tier is a client that contains query and reporting tools. Another new layer is added for faster results. This is done with the help of query classification technique.

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Automatic web query classification using labeled and unlabeled training data

Cited by 64 publications

References 4 publications

Explicit web search result diversification

Explicit web search result diversification

Web Supported Query Taxonomy Classifier

Taxonomy based Data Marts

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