Abstract not found

We systematically investigate a new approach to estimating the parameters of language models for information retrieval, called parsimonious language models. Parsimonious language models explicitly address the relation between levels of language models that are typically used for smoothing. As such, they need fewer (non-zero) parameters to describe the data. We apply parsimonious models at three stages of the retrieval process:1) at indexing time; 2) at search time; 3) at feedback time. Experimental results show that we are able to build models that are significantly smaller than standard models, but that still perform at least as well as the standard approaches.

This paper investigates the pre-conditions for successful combination of document representations formed from structural markup for the task of known-item search. As this task is very similar to work in meta-search and data fusion, we adapt several hypotheses from those research areas and investigate them in this context. To investigate these hypotheses, we present a mixturebased language model and also examine many of the current metasearch algorithms. We find that compatible output from systems is important for successful combination of document representations. We also demonstrate that combining low performing document representations can improve performance, but not consistently. We find that the techniques best suited for this task are robust to the inclusion of poorly performing document representations. We also explore the role of variance of results across systems and its impact on the performance of fusion, with the surprising result that the correct documents have higher variance across document representations than highly ranking incorrect documents.

Discriminative models have been preferred over generative models in many machine learning problems in the recent past owing to some of their attractive theoretical properties. In this paper, we explore the applicability of discriminative classifiers for IR. We have compared the performance of two popular discriminative models, namely the maximum entropy model and support vector machines with that of language modeling, the state-of-the-art generative model for IR. Our experiments on ad-hoc retrieval indicate that although maximum entropy is significantly worse than language models, support vector machines are on par with language models. We argue that the main reason to prefer SVMs over language models is their ability to learn arbitrary features automatically as demonstrated by our experiments on the home-page finding task of TREC-10.

Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a structural re-ranking approach to ad hoc information retrieval: we reorder the documents in an initially retrieved set by exploiting asymmetric relationships between them. Specifically, we consider generation links, which indicate that the language model induced from one document assigns high probability to the text of another; in doing so, we take care to prevent bias against long documents. We study a number of re-ranking criteria based on measures of centrality in the graphs formed by generation links, and show that integrating centrality into standard language-model-based retrieval is quite effective at improving precision at top ranks.

It has been observed that anchor text in web documents is very useful in improving the quality of web text search for some classes of queries. By examining properties of anchor text in a large intranet, we hope to shed light on why this is the case. Our main premise is that anchor text behaves very much like real user queries and consensus titles. Thus an understanding of how anchor text is related to a document will likely lead to better understanding of how to translate a user's query into high quality search results. Our approach is experimental, based on a study of a large corporate intranet, including the content as well as a large stream of queries against that content. We conduct experiments to investigate several aspects of anchor text, including their relationship to titles, the frequency of queries that can be satisfied by anchortext alone, and the homogeneity of results fetched by anchor text.

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.

This paper follows a formal approach to information retrieval based on statistical language models. By introducing some simple reformulations of the basic language modeling approach we introduce the notion of importance of a query term. The importance of a query term is an unknown parameter that explicitly models which of the query terms are generated from the relevant documents (the important terms), and which are not (the unimportant terms). The new language modeling approach is shown to explain a number of practical facts of today’s information retrieval systems that are not very well explained by the current state of information retrieval theory, including stop words, mandatory terms, coordination level ranking and retrieval using phrases.

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.

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.