Statistical language models have been proposed recently for several information retrieval tasks, including the resource selection task in distributed information retrieval. This paper extends the language modeling approach to integrate resource selection, ad-hoc searching, and merging of results from different text databases into a single probabilistic retrieval model. This new approach is designed primarily for Intranet environments, where it is reasonable to assume that resource providers are relatively homogeneous and can adopt the same kind of search engine. Experiments demonstrate that this new, integrated approach is at least as effective as the prior state-of-the-art in distributed IR.

We give a uni ed account of the probabilistic semantics underlying the language modeling approach and the traditional probabilistic model for information retrieval, showing that the two approaches can be viewed as being equivalent probabilistically, since they are based on dierent factorizations of the same generative relevance model. We also discuss how the two approaches lead to dierent retrieval frameworks in practice, since they involve component models that are estimated quite dierently.

This article presents a unified approach to Chinese statistical language modeling (SLM). Applying SLM techniques like trigram language models to Chinese is challenging because (1) there is no standard definition of words in Chinese; (2) word boundaries are not marked by spaces; and (3) there is a dearth of training data. Our unified approach automatically and consistently gathers a high-quality training data set from the Web, creates a high-quality lexicon, segments the training data using this lexicon, and compresses the language model, all by using the maximum likelihood principle, which is consistent with trigram model training. We show that each of the methods leads to improvements over standard SLM, and that the combined method yields the best pinyin conversion result reported.

Abstract not found

This paper presents a new discriminative model for information retrieval (IR), referred to as linear discriminant model (LDM), which provides a flexible framework to incorporate arbitrary features. LDM is different from most existing models in that it takes into account a variety of linguistic features that are derived from the component models of HMM that is widely used in language modeling approaches to IR. Therefore, LDM is a means of melding discriminative and generative models for IR. We present two algorithms of parameter learning for LDM. One is to optimize the average precision (AP) directly using an iterative procedure. The other is a perceptron-based algorithm that minimizes the number of discordant document-pairs in a rank list. The effectiveness of our approach has been evaluated on the task of ad hoc retrieval using six English and Chinese TREC test sets. Results show that (1) in most test sets, LDM significantly outperforms the state-of-the-art language modeling approaches and the classical probabilistic retrieval model; (2) it is more appropriate to train LDM using a measure of AP rather than likelihood if the IR system is graded on AP; and (3) linguistic features (e.g. phrases and dependences) are effective for IR if they are incorporated properly.

Previous research has shown that passage-level evidence can bring added benefits to document retrieval when documents are long or span different subject areas. Recent developments in language modeling approach to IR provided a new effective alternative to traditional retrieval models. These two streams of research motivate us to examine the use of passages in a language model framework. This paper reports on experiments using passages in a simple language model and a relevance model, and compares the results with document-based retrieval. Results from the INQUERY search engine, which is not based on a language modeling approach, are also given for comparison. Test data include two heterogeneous and one homogeneous document collections. Our experiments show that passage retrieval is feasible in the language modeling context, and more importantly, it can provide more reliable performance than retrieval based on full documents.

This paper presents a novel probabilistic information retrieval framework in which the retrieval problem is formally treated as a statistical decision problem. In this framework, queries and documents are modeled using statistical language models (i.e., probabilistic models of text), user preferences are modeled through loss functions, and retrieval is cast as a risk minimization problem. We discuss how this framework can unify existing retrieval models and accommodate the systematic development of new retrieval models. As an example of using the framework to model non-traditional retrieval problems, we derive new retrieval models for subtopic retrieval, which is concerned with retrieving documents to cover many different subtopics of a general query topic. These new models differ from traditional retrieval models in that they go beyond independent topical relevance.

In this paper, we propose a new language model, namely, a title language model, for information retrieval. Different from the traditional language model used for retrieval, we define the conditional probability P(Q|D) as the probability of using query Q as the title for document D. We adopted the statistical translation model learned from the title and document pairs in the collection to compute the probability P(Q|D). To avoid the sparse data problem, we propose two new smoothing methods. In the experiments with four different TREC document collections, the title language model for information retrieval with the new smoothing method outperforms both the traditional language model and the vector space model for IR significantly.

Many approaches to personalization involve learning short-term and long-term user models. The user models provide context for queries and other interactions with the information system. In this paper, we discuss how language models can be used to represent context and support context-based techniques such as relevance feedback and query disambiguation. 1. Overview From some perspectives, personalization has been studied in information retrieval for some time. If the goal of personalization is to improve the effectiveness of information access by adapting to individual users' needs, then techniques such as relevance feedback and filtering would certainly be considered to support personalization. There has also been considerable research done, mostly in the 1980s, on user modeling for information retrieval. This research had essentially the same goal as current research on personalization, which is to build a model of a user's interests and preferences over time. Filtering systems, too...

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.