We explore the relationship between time and relevance using TREC ad-hoc queries. A type of query is identified that favors very recent documents. We propose a time-based language model approach to retrieval for these queries. We show how time can be incorporated into both query-likelihood models and relevance models. We carried out experiments to compare time-based language models to heuristic techniques for incorporating document recency in the ranking. Our results show that time-based models perform as well as or better than the best of the heuristic techniques.

The language modeling approach to retrieval has been shown to perform well empirically. One advantage of this new approach is its statistical foundations. However, feedback, as one important component in a retrieval system, has only been dealt with heuristically in this new retrieval approach: the original query is usually literally expanded by adding ditional terms to it. Such expansion-based feedback creates an inconsistent interpretation of the original and the expanded query. In this paper, we present a more principled approach to feedback in the language modeling approach. Specifically, we treat feedback as updating the query language model based on the extra evidence carried by the feedback documents. Such a model-based feedback strategy easily fits into an extension of the language modeling approach. We propose and evaluate two different approaches to updating a query language model based on feedback documents, one based on a generarive probabilistic model of feedback documents and one based on minimization of the KL-divergence over feedback documents. Experiment resuits show that both approaches are effective and outperform the Rocchio feedback approach.

An important class of searches on the world-wide-web has the goal to find an entry page (homepage) of an organisation. Entry page search is quite different from Ad Hoc search. Indeed a plain Ad Hoc system performs disappointingly. We explored three non-content features of web pages: page length, number of incoming links and URL form. Especially the URL form proved to be a good predictor. Using URL form priors we found over 70% of all entry pages at rank 1, and up to 89% in the top 10. Non-content features can easily be embedded in a language model framework as a prior probability.

The social impact from the World Wide Web cannot be underestimated, but technologies used to build the Web are also revolutionizing the sharing of business and government information within intranets. In many ways the lessons learned from the Internet carry over directly to intranets, but others do not apply. In particular, the social forces that guide the development of intranets are quite di#erent, and the determination of a "good answer" for intranet search is quite di#erent than on the Internet. In this paper we study the problem of intranet search. Our approach focuses on the use of rank aggregation, and allows us to examine the e#ects of di#erent heuristics on ranking of search results.

We augment naive Bayes models with statistical n-gram language models to address shortcomings of the standard naive Bayes text classifier. The result is a generalized naive Bayes classifier

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For this year's web track, we concentrated on the entry page finding task. For the content-only runs, in both the ad-hoc task and the entry page finding task, we used an information retrieval system based on a simple unigram language model. In the Ad hoc task we experimented with alternatieve approaches to smoothing. For the entry page task, we incorporated additional information into the model. The sources of information we used in addition to the document's content are links, URLs and anchors. We found that almost every approach can improve the results of a content only run. In the end, a very basic approach, using the depth of the path of the URL as a prior, yielded by far the largest improvement over the content only results.

Although more and more language pairs are covered by machine translation (MT) services, there are still many pairs that lack translation resources. Cross-language information retrieval (CLIR) is an application that needs translation functionality of a relatively low level of sophistication, since current models for information retrieval (IR) are still based on a bag of words. The Web provides a vast resource for the automatic construction of parallel corpora that can be used to train statistical translation models automatically. The resulting translation models can be embedded in several ways in a retrieval model. In this article, we will investigate the problem of automatically mining parallel texts from the Web and different ways of integrating the translation models within the retrieval process. Our experiments on standard test collections for CLIR show that the Web-based translation models can surpass commercial MT systems in CLIR tasks. These results open the perspective of constructing a fully automatic query translation device for CLIR at a very low cost. 1.

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XML retrieval is a departure from standard document retrieval in which each individual XML element, ranging from italicized words or phrases to full blown articles, is a potentially retrievable unit. The distribution of XML element lengths is unlike what we usually observe in standard document collections, prompting us to revisit the issue of document length normalization. We perform a comparative analysis of arbitrary elements versus relevant elements, and show the importance of length as a parameter for XML retrieval. Within the language modeling framework, we investigate a range of techniques that deal with length either directly or indirectly. We observe a length bias introduced by the amount of smoothing, and show the importance of extreme length priors for XML retrieval. We also show that simply removing shorter elements from the index (by introducing a cut-o# value) does not create an appropriate document length normalization. Even after increasing the minimal size of XML elements occurring in the index, the importance of an extreme length bias remains.