Query reformulation has been studied as a domain independent task. Existing work attempts to expand a query or substitute its terms with the same set of candidates regardless of the domain of this query. Since terms might be semantically related in one domain but not in others, it is more effective to provide candidates for queries with respect to their domain. This paper demonstrates the advantage of this domain dependent query reformulation approach, which learns its candidates, using a standard technique, for each domain from a separate sample of data derived automatically from a generic query log. Our results show that our approach statistically significantly outperforms the domain independent approach, which learns to reformulate from the same log using the same technique, on a large query set consisting of both health and commerce queries. Our results have very practical interpretation: while building different reformulation systems to handle queries from different domains does not require additional manual effort, it provides substantially better retrieval effectiveness than having a single system handling all queries. Additionally, we show that leveraging domain specific manually labelled data leads to further improvement.

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 NDCG@5). However, query reduction on the web is hampered by the lack of accurate query performance predictors and the constraints imposed by search engine architectures and ranking algorithms.

Predicting the performance of web queries is useful for several applications such as automatic query reformulation and automatic spell correction. In the web environment, accurate performance prediction is challenging because measures such as clarity that work well on homogeneous TREC-like collections, are not as effective and are often expensive to compute. We present Rank-time Performance Prediction (RAPP), an effective and efficient approach for online performance prediction on the web. RAPP uses retrieval scores, and aggregates of the rank-time features used by the document- ranking algorithm to train regressors for query performance prediction. On a set of over 12,000 queries sampled from the query logs of a major search engine, RAPP achieves a linear correlation of 0.78 with [email protected], and 0.52 with [email protected] Analysis of prediction accuracy shows that hard queries are easier to identify while easy queries are harder to identify.

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 reranking 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. We find that it is possible to correctly predict the top label better than one in five times, even when no past query trail exactly matches the long and rare query. We show that these labels can be used to reorder top-ranked search results leading to a significant improvement in retrieval performance over baselines that do not utilize query labeling, but instead rank results using content-matching or click-through logs. The outcomes of our research have implications for search providers attempting to provide users with highly-relevant search results for long queries.

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 selecting 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 sub-query chosen by the ranker results in a statistically significant average improvement of 8% on our test sets. Analysis of the results shows that query reduction is well-suited for moderately-performing long queries, and a small set of query quality predictors are well-suited for the task of ranking sub-queries.

Handling long queries can involve either pruning the query to retain only the important terms (reduction), or expanding the query to include related concepts (expansion). While automatic techniques to do so exist, roughly 25% performance improvements in terms of MAP have been realized in past work through interactive variants. We show that selectively reducing or expanding a query leads to an average improvement of 51% in MAP over the baseline for standard TREC test collections. We demonstrate how user interaction can be used to achieve this improvement. Most interaction techniques present users with a fixed number of options for all queries. We achieve improvements by interacting less with the user, i.e., we present techniques to identify the optimal number of options to present to users, resulting in an interface with an average of 70% fewer options to consider. Previous algorithms supporting interactive reduction and expansion are exponential in nature. To extend their utility to operational environments, we present techniques to make the complexity of the algorithms polynomial. We finally present an analysis of long queries that continue to exhibit poor performance in spite of our new techniques.

poster Share on Selective user interaction Authors: Giridhar Kumaran University of Massachusetts Amherst, Amherst, MA University of Massachusetts Amherst, Amherst, MAView Profile , James Allan University of Massachusetts Amherst, Amherst, MA University of Massachusetts Amherst, Amherst, MAView Profile Authors Info & Claims CIKM '07: Proceedings of the sixteenth ACM conference on Conference on information and knowledge managementNovember 2007 Pages 923–926https://doi.org/10.1145/1321440.1321576Online:06 November 2007Publication History 8citation210DownloadsMetricsTotal Citations8Total Downloads210Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We explore interactive methods to further improve the performance of pseudo-relevance feedback. Studies \citeria suggest that new methods for tackling difficult queries are required. Our approach is to gather more information about the query from the user by asking her simple questions. The equally simple responses are used to modify the original query. Our experiments using the TREC Robust Track queries show that we can obtain a significant improvement in mean average precision averaging around 5% over pseudo-relevance feedback. This improvement is also spread across more queries compared to ordinary pseudo-relevance feedback, as suggested by geometric mean average precision.

Depending on a web searcher's familiarity with a query's target topic, it may be more appropriate to show her introductory or advanced documents. The TREC HARD [1] track defined topic familiarity as meta-data associated with a user's query. We instead define a user-independent and query-independent model of topic-familiarity required to read a document, so it can be matched to a given user in response to a query. An introductory web page is defined as A web page that doesn't presuppose any background knowledge of the topic it is on, and to an extent introduces or defines the key terms in the topic. while an advanced web page is defined as A web page that assumes sufficient background knowledge of the topic it is on, and familiarity with the key technical/ important terms in the topic, and potentially builds on them. We develop a method for biasing the initial mix of documents returned by a search engine to increase the number of documents of desired familiarity level up to position 5, and up to position 10. Our method involves building a supervised text classifier, incorporating features based on reading level, the distribution of stop-words in the text, and non-text features such as average line-length. Using this familiarity classifier, we achieve statistically significant improvements at reranking the result set to show introductory documents higher up the ranked list. Our classifier can be seamlessly integrated into current search engine technology without involving any major modifications to existing architectures.

New Event Detection is a challenging task that still offers scope for great improvement after years of effort. In this paper we show how performance on New Event Detection (NED) can be improved by the use of text classification techniques as well as by using named entities in a new way. We explore modifications to the document representation in a vector space-based NED system. We also show that addressing named entities preferentially is useful only in certain situations. A combination of all the above results in a multi-stage NED system that performs much better than baseline single-stage NED systems.

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The classical approach to the Economic Load Dispatch Problem (ELDP) seeks to minimize the cost of generation subject to the usual constraints. If the transmission losses are also to be taken care of, a common method (/spl lambda/-iteration procedure) involves adding the cost of transmission losses charged at incremental cost of received power to the cost of generation. This combined cost function forms the objective function to be minimized, However it is desirable that the transmission losses be dealt with separately in the minimization process, without computing the incremental cost of received power. Genetic Algorithms (GAs) offer a suitable and robust approach to meet the twin objectives of cost minimization and loss minimization simultaneously. This paper presents the development of the GA for solving the ELDP. It also suggests a convenient technique for representing the search space. Finally, the results of the algorithm are analyzed by comparing them with those obtained from a direct random search algorithm, called the LJ algorithm.