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.

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.

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.

This thesis explores approaches to the problem of spoken document retrieval (SDR), which is the task of automatically indexing and then retrieving relevant items from a large collection of recorded speech messages in response to a user specified natural language text query. We investigate the use of subword unit representations for SDR as an alternative to words generated by either keyword spotting or continuous speech recognition. Our investigation is motivated by the observation that word-based retrieval approaches face the problem of either having to know the keywords to search for a priori, or requiring a very large recognition vocabulary in order to cover the contents of growing and diverse message collections. The use of subword units in the recognizer constrains the size of the vocabulary needed to cover the language; and the use of subword units as indexing terms allows for the detection of new user-specified query terms during retrieval. Four

This paper describes the ocial runs of the Twenty-One group for TREC-8. The Twenty-One group participated in the Ad-hoc, CLIR, Adaptive Filtering and SDR tracks. The main focus of our experiments is the development and evaluation of retrieval methods that are motivated by natural language processing techniques. The following new techniques are introduced in this paper. In the Ad-Hoc and CLIR tasks we experimented with automatic sense disambiguation followed by query expansion or translation. We used a combination of thesaurial and corpus information for the disambiguation process. We continued research on CLIR techniques which exploit the target corpus for an implicit disambiguation, by importing the translation probabilities into the probabilistic term-weighting framework. In ltering we extended the the use of language models for document ranking with a relevance feedback algorithm for query term reweighting. 1 Introduction Twenty-One 1 is a project funded by the EU Tele...

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.

Abstract not found

Abstract not found

This paper presents a novel statistical model for crosslanguage information retrieval. Given a written query in the source language, documents in the target language are ranked by integrating probabilities computed by two statistical models: a query-translation model, which generates most probable term-by-term translations of the query, and a query-document model, which evaluates the likelihood of each document and translation. Integration of the two scores is performed over the set of N most probable translations of the query. Experimental results with values N = 1, 5, 10 are presented on the Italian-English bilingual track data used in the CLEF 2000 and 2001 evaluation campaigns.