Query reformulation techniques based on query logs have been studied as a method of capturing user intent and improving retrieval effectiveness. The evaluation of these techniques has primarily, however, focused on proprietary query logs and selected samples of queries. In this paper, we suggest that anchor text, which is readily available, can be an effective substitute for a query log and study the effectiveness of a range of query reformulation techniques (including log-based stemming, substitution, and expansion) using standard TREC collections. Our results show that logbased query reformulation techniques are indeed effective with standard collections, but expansion is a much safer form of query modification than word substitution. We also show that using anchor text as a simulated query log is as least as effective as a real log for these techniques.

Long queries frequently contain many extraneous terms that hinder retrieval of relevant documents. We present techniques to reduce long queries to more effective shorter ones that lack those extraneous terms. Our work is motivated by the observation that perfectly reducing long TREC description queries can lead to an average improvement of 30 % in mean average precision. Our approach involves transforming the reduction problem into a problem of learning to rank all sub-sets of the original query (sub-queries) based on their predicted quality, and select the top sub-query. We use various measures of query quality described in the literature as features to represent sub-queries, and train a classifier. Replacing the original long query with the top-ranked subquery chosen by the ranking classifier results in a statistically significant average improvement of 8 % on our test sets. Analysis of the results shows that query reduction is wellsuited for moderately-performing long queries, and a small set of query quality predictors are well-suited for the task of ranking sub-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 (upto 25 % improvement in

The probability that a term appears in relevant documents (P� � | ��) is a fundamental quantity in several probabilistic retrieval models, however it is difficult to estimate without relevance judgments or a relevance model. We call this value term necessity because it measures the percentage of relevant documents retrieved by the term – how necessary a term’s occurrence is to document relevance. Prior research typically either set this probability to a constant, or estimated it based on the term's inverse document frequency, neither of which was very effective. This paper identifies several factors that affect term necessity, for example, a term’s topic centrality, synonymy and abstractness. It develops term- and query-dependent features for each factor that enable supervised learning of a predictive model of term necessity from training data. Experiments with two popular retrieval models and 6 standard datasets demonstrate that using predicted term necessity estimates as user term weights of the original query terms leads to significant improvements in retrieval accuracy.

The probability that a term appears in a relevant document is a fundamental quantity in the theory of probabilistic information retrieval, however prior research provided few clues about how to estimate it reliably. Since this probability measures how likely it is that a term has to appear in a document in order for the document to be relevant, in this thesis, it is called term necessity. Equivalently, it is also the proportion of relevant documents that contain the term, thus measures term recall, or the complement of term mismatch. This thesis uses exploratory data analysis to identify common reasons that user-specified query terms fail to match relevant documents, develops features correlated with each reason, and integrates them into a model that can be trained from data. The resulting term necessity predictions can be used as term weights in state-of-the-art retrieval models to improve retrieval accuracy substantially. Feature-based necessity prediction also supports diagnosis and improvement of query components. The thesis research will develop several forms of diagnosis and intervention. The simplest form is interactive feedback in which potential problems with query components are identified for a person to fix. More nuanced approaches to automatic formulations

We present a new supervised method for estimating term-based retrieval models and apply it to weight expansion terms from relevance feedback. While previous work on supervised feedback [Cao et al., 2008] demonstrated significantly improved retrieval accuracy over standard unsupervised approaches [Lavrenko and Croft, 2001, Zhai and Lafferty, 2001], feedback terms were assumed to be independent in order to reduce training time. In contrast, we adapt the AdaRank learning algorithm [Xu and Li, 2007] to simultaneously estimate parameterization of all feedback terms. While not evaluated here, the method can be more generally applied for joint estimation of both query and feedback terms. To apply our method to a large web collection, we also investigate use of sampling to reduce feature extraction time while maintaining robust learning. 1

The probability that a term appears in relevant documents ( ) is a fundamental quantity in several probabilistic retrieval models, however it is difficult to estimate without relevance judgments or a relevance model. We call this value term necessity because it measures the percentage of relevant documents retrieved by the term – how necessary a term‟s occurrence is to document relevance. Prior research typically either set this probability to a constant, or estimated it based on the term's inverse document frequency, neither of which was very effective. This paper identifies several factors that affect term necessity, for example, a term‟s topic centrality, synonymy and abstractness. It develops term- and query-dependent features for each factor that enable supervised learning of a predictive model of term necessity from training data. Experiments with two popular retrieval models and 6 standard datasets demonstrate that using predicted term necessity estimates as user term weights of the original query terms leads to significant improvements in retrieval accuracy.

Abstract. A well-known challenge of information retrieval is how to infer a user’s underlying information need when the input query consists of only a few keywords. Question Answering (QA) systems face an equally important but opposite challenge: given a verbose question, how can the system infer the relative importance of terms in order to differentiate the core information need from supporting context? We investigate three simple term-weighting schemes for such estimation within the language modeling retrieval paradigm [6]. While the three schemes described are ad hoc, they address a principled estimation problem underlying the standard word unigram model. We also show these schemes enable better estimation of a state-of-the-art class model based on term clustering [5]. Using a TREC QA dataset, we evaluate the three weighting schemes for both word and class models on the QA subtask of sentence retrieval. Our inverse sentence frequency weighting scheme achieves over 5 % absolute improvement in mean-average precision for the standard word model and nearly 2 % absolute improvement for the class model. 1

Web search engines can perform poorly for long queries (i.e., those containing four or more terms), in part because of their high level of query specificity. The automatic assignment of labels to long queries can capture aspects of a user’s search intent that may not be apparent from the terms in the query. This affords search result matching or re-ranking based on queries and labels rather than the query text alone. Query labels can be derived from interaction logs generated from many users ’ search result clicks or from query trails comprising the chain of URLs visited following query submission. However, since long queries are typically rare, they are difficult to label in this way because little or no historic log data exists for them. A subset of these queries may be amenable to labeling by detecting similarities between parts of a long and rare query and the queries which appear in logs. In this article, we present the comparison of four similarity algorithms for the automatic assignment of Open Directory Project category labels to long and rare queries, based solely on matching against similar satisfied query trails extracted from log data. Our findings show that although the similarity-matching algorithms we investigated have tradeoffs in terms of coverage and accuracy, one algorithm that bases similarity on a popular search result ranking function (effectively regarding potentially-similar queries as “documents”) outperforms the others.