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Exploring Reductions for Long Web Queries
"... Long queries form a difficult, but increasingly important segment for web search engines. Query reduction, a technique for dropping unnecessary query terms from long queries, improves performance of ad-hoc retrieval on TREC collections. Also, it has great potential for improving long web queries (up ..."
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Cited by 21 (1 self)
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Long queries form a difficult, but increasingly important segment for web search engines. Query reduction, a technique for dropping unnecessary query terms from long queries, improves performance of ad-hoc retrieval on TREC collections. Also, it has great potential for improving long web queries (upto 25 % improvement in
Mapping queries to the Linking Open Data cloud: A case study using DBpedia.
- J. Web Sem.
, 2011
"... a b s t r a c t We introduce the task of mapping search engine queries to DBpedia, a major linking hub in the Linking Open Data cloud. We propose and compare various methods for addressing this task, using a mixture of information retrieval and machine learning techniques. Specifically, we present ..."
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a b s t r a c t We introduce the task of mapping search engine queries to DBpedia, a major linking hub in the Linking Open Data cloud. We propose and compare various methods for addressing this task, using a mixture of information retrieval and machine learning techniques. Specifically, we present a supervised machine learning-based method to determine which concepts are intended by a user issuing a query. The concepts are obtained from an ontology and may be used to provide contextual information, related concepts, or navigational suggestions to the user submitting the query. Our approach first ranks candidate concepts using a language modeling for information retrieval framework. We then extract query, concept, and search-history feature vectors for these concepts. Using manual annotations we inform a machine learning algorithm that learns how to select concepts from the candidates given an input query. Simply performing a lexical match between the queries and concepts is found to perform poorly and so does using retrieval alone, i.e., omitting the concept selection stage. Our proposed method significantly improves upon these baselines and we find that support vector machines are able to achieve the best performance out of the machine learning algorithms evaluated.
Improving verbose queries using subset distribution
- In Proc. CIKM
, 2010
"... Dealing with verbose (or long) queries poses a new challenge for information retrieval. Selecting a subset of the original query (a “sub-query”) has been shown to be an effective method for improving these queries. In this paper, the distribution of sub-queries (“subset distribution”) is formally mo ..."
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Cited by 18 (4 self)
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Dealing with verbose (or long) queries poses a new challenge for information retrieval. Selecting a subset of the original query (a “sub-query”) has been shown to be an effective method for improving these queries. In this paper, the distribution of sub-queries (“subset distribution”) is formally modeled within a well-grounded framework. Specifically, sub-query selection is considered as a sequential labeling problem, where each query word in a verbose query is assigned a label of “keep ” or “don’t keep”. A novel Conditional Random Field model is proposed to generate the distribution of sub-queries. This model captures the local and global dependencies between query words and directly optimizes the expected retrieval performance on a training set. The experiments, based on different retrieval models and performance measures, show that the proposed model can generate high-quality sub-query distributions and can significantly outperform state-of-the-art techniques.
Evaluating verbose query processing techniques
- In Proc. of SIGIR, SIGIR ’10
, 2010
"... Verbose or long queries are a small but significant part of the query stream in web search, and are common in other applications such as collaborative question answering (CQA). Current search engines perform well with keyword queries but are not, in general, effective for verbose queries. In this pa ..."
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Cited by 15 (3 self)
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Verbose or long queries are a small but significant part of the query stream in web search, and are common in other applications such as collaborative question answering (CQA). Current search engines perform well with keyword queries but are not, in general, effective for verbose queries. In this paper, we examine query processing techniques which can be applied to verbose queries prior to submission to a search engine in order to improve the search engine’s results. We focus on verbose queries that have sentence-like structure, but are not simple “wh- ” questions, and assume the search engine is a “black box. ” We evaluated the output of two search engines using queries from a CQA service and our results show that, among a broad range of techniques, the most effective approach is to simply reduce the length of the query. This can be achieved effectively by removing “stop structure ” instead of only stop words. We show that the process of learning and removing stop structure from a query can be effectively automated.
Learning Semantic Query Suggestions
"... Abstract. An important application of semantic web technology is recognizing human-defined concepts in text. Query transformation is a strategy often used in search engines to derive queries that are able to return more useful search results than the original query and most popular search engines pr ..."
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Abstract. An important application of semantic web technology is recognizing human-defined concepts in text. Query transformation is a strategy often used in search engines to derive queries that are able to return more useful search results than the original query and most popular search engines provide facilities that let users complete, specify, or reformulate their queries. We study the problem of semantic query suggestion, a special type of query transformation based on identifying semantic concepts contained in user queries. We use a feature-based approach in conjunction with supervised machine learning, augmenting term-based features with search history-based and concept-specific features. We apply our method to the task of linking queries from real-world query logs (the transaction logs of the Netherlands Institute for Sound and Vision) to the DBpedia knowledge base. We evaluate the utility of different machine learning algorithms, features, and feature types in identifying semantic concepts using a manually developed test bed and show significant improvements over an already high baseline. The resources developed for this paper, i.e., queries, human assessments, and extracted features, are available for download. 1
Structural annotation of search queries using pseudo-relevance feedback
- In Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM ’10
, 2010
"... Marking up queries with annotations such as part-of-speech tags, capitalization, and segmentation, is an important part of many approaches to query processing and understanding. Due to their brevity and idiosyncratic structure, search queries pose a challenge to existing annotation tools that are co ..."
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Cited by 6 (1 self)
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Marking up queries with annotations such as part-of-speech tags, capitalization, and segmentation, is an important part of many approaches to query processing and understanding. Due to their brevity and idiosyncratic structure, search queries pose a challenge to existing annotation tools that are commonly trained on full-length documents. To address this challenge, we view the query as an explicit representation of a latent information need, which allows us to use pseudorelevance feedback, and to leverage additional information from the document corpus, in order to improve the quality of query annotation.
Joint Annotation of Search Queries
"... Marking up search queries with linguistic annotations such as part-of-speech tags, capitalization, and segmentation, is an important part of query processing and understanding in information retrieval systems. Due to their brevity and idiosyncratic structure, search queries pose a challenge to exist ..."
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Cited by 6 (1 self)
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Marking up search queries with linguistic annotations such as part-of-speech tags, capitalization, and segmentation, is an important part of query processing and understanding in information retrieval systems. Due to their brevity and idiosyncratic structure, search queries pose a challenge to existing NLP tools. To address this challenge, we propose a probabilistic approach for performing joint query annotation. First, we derive a robust set of unsupervised independent annotations, using queries and pseudo-relevance feedback. Then, we stack additional classifiers on the independent annotations, and exploit the dependencies between them to further improve the accuracy, even with a very limited amount of available training data. We evaluate our method using a range of queries extracted from a web search log. Experimental results verify the effectiveness of our approach for both short keyword queries, and verbose natural language queries. 1
Search in the Lost Sense of “Query”: Question Formulation in Web Search Queries and its Temporal Changes
"... Web search is an information-seeking activity. Often times, this amounts to a user seeking answers to a question. However, queries, which encode user’s information need, are typically not expressed as full-length natural language sentences — in particular, as questions. Rather, they consist of one o ..."
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Cited by 5 (1 self)
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Web search is an information-seeking activity. Often times, this amounts to a user seeking answers to a question. However, queries, which encode user’s information need, are typically not expressed as full-length natural language sentences — in particular, as questions. Rather, they consist of one or more text fragments. As humans become more searchengine-savvy, do natural-language questions still have a role to play in web search? Through a systematic, large-scale study, we find to our surprise that as time goes by, web users are more likely to use questions to express their search intent. 1
When Web Search Fails, Searchers Become Askers: Understanding the Transition
"... While Web search has become increasingly effective over the last decade, for many users ’ needs the required answers may be spread across many documents, or may not exist on the Web at all. Yet, many of these needs could be addressed by asking people via popular Community Question Answering (CQA) se ..."
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Cited by 4 (1 self)
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While Web search has become increasingly effective over the last decade, for many users ’ needs the required answers may be spread across many documents, or may not exist on the Web at all. Yet, many of these needs could be addressed by asking people via popular Community Question Answering (CQA) services, such as Baidu Knows, Quora, or Yahoo! Answers. In this paper, we perform the first large-scale analysis of how searchers become askers. For this, we study the logs of a major web search engine to trace the transformation of a large number of failed searches into questions posted on a popular CQA site. Specifically, we analyze the characteristics of the queries, and of the patterns of search behavior that precede posting a question; the relationship between the content of the attempted queries and of the posted questions; and the subsequent actions the user performs on the CQA site. Our work develops novel insights into searcher intent and behavior that lead to asking questions to the community, providing a foundation for more effective integration of automated web search and social information seeking.
