Document retrieval

About: Document retrieval is a research topic. Over the lifetime, 6821 publications have been published within this topic receiving 214383 citations.

A language modeling approach to information retrieval

Abstract: In today's world, there is no shortage of information. However, for a specific information need, only a small subset of all of the available information will be useful. The field of information retrieval (IR) is the study of methods to provide users with that small subset of information relevant to their needs and to do so in a timely fashion. Information sources can take many forms, but this thesis will focus on text based information systems and investigate problems germane to the retrieval of written natural language documents. Central to these problems is the notion of "topic." In other words, what are documents about? However, topics depend on the semantics of documents and retrieval systems are not endowed with knowledge of the semantics of natural language. The approach taken in this thesis will be to make use of probabilistic language models to investigate text based information retrieval and related problems. One such problem is the prediction of topic shifts in text, the topic segmentation problem. It will be shown that probabilistic methods can be used to predict topic changes in the context of the task of new event detection. Two complementary sets of features are studied individually and then combined into a single language model. The language modeling approach allows this problem to be approached in a principled way without complex semantic modeling. Next, the problem of document retrieval in response to a user query will be investigated. Models of document indexing and document retrieval have been extensively studied over the past three decades. The integration of these two classes of models has been the goal of several researchers but it is a very difficult problem. Much of the reason for this is that the indexing component requires inferences as to the semantics of documents. Instead, an approach to retrieval based on probabilistic language modeling will be presented. Models are estimated for each document individually. The approach to modeling is non-parametric and integrates the entire retrieval process into a single model. One advantage of this approach is that collection statistics, which are used heuristically for the assignment of concept probabilities in other probabilistic models, are used directly in the estimation of language model probabilities in this approach. The language modeling approach has been implemented and tested empirically and performs very well on standard test collections and query sets. In order to improve retrieval effectiveness, IR systems use additional techniques such as relevance feedback, unsupervised query expansion and structured queries. These and other techniques are discussed in terms of the language modeling approach and empirical results are given for several of the techniques developed. These results provide further proof of concept for the use of language models for retrieval tasks.

Okapi at TREC

Abstract: City submitted two runs each for the automatic ad hoc, very large collection track, automatic routing and Chinese track; and took part in the interactive and filtering tracks. The method used was : expansion using terms from the top documents retrieved by a pilot search on topic terms. Additional runs seem to show that we would have done better without expansion. Twor runs using the method of city96al were also submitted for the Very Large Collection track. The training database and its relevant documents were partitioned into three parts. Working on a pool of terms extracted from the relevant documents for one partition, an iterative procedure added or removed terms and/or varied their weights. After each change in query content or term weights a score was calculated by using the current query to search a second protion of the training database and evaluating the results against the corresponding set of relevant documents. Methods were compared by evaluating queries predictively against the third training partition. Queries from different methods were then merged and the results evaluated in the same way. Two runs were submitted, one based on character searching and the other on words or phrases. Much of the work involved investigating plausible methods of applying Okapi-style weighting to phrases

The use of MMR, diversity-based reranking for reordering documents and producing summaries

Abstract: This paper presents a method for combining query-relevance with information-novelty in the context of text retrieval and summarization. The Maximal Marginal Relevance (MMR) criterion strives to reduce redundancy while maintaining query relevance in re-ranking retrieved documents and in selecting apprw priate passages for text summarization. Preliminary results indicate some benefits for MMR diversity ranking in document retrieval and in single document summarization. The latter are borne out by the recent results of the SUMMAC conference in the evaluation of summarization systems. However, the clearest advantage is demonstrated in constructing non-redundant multi-document summaries, where MMR results are clearly superior to non-MMR passage selection.

The Probabilistic Relevance Framework

Abstract: The Probabilistic Relevance Framework (PRF) is a formal framework for document retrieval, grounded in work done in the 1970—1980s, which led to the development of one of the most successful text-retrieval algorithms, BM25. In recent years, research in the PRF has yielded new retrieval models capable of taking into account document meta-data (especially structure and link-graph information). Again, this has led to one of the most successful Web-search and corporate-search algorithms, BM25F. This work presents the PRF from a conceptual point of view, describing the probabilistic modelling assumptions behind the framework and the different ranking algorithms that result from its application: the binary independence model, relevance feedback models, BM25 and BM25F. It also discusses the relation between the PRF and other statistical models for IR, and covers some related topics, such as the use of non-textual features, and parameter optimisation for models with free parameters.

The design of browsing and berrypicking techniques for the online search interface

Abstract: First, a new model of searching in online and other information systems, called ‘berrypicking’, is discussed. This model, it is argued, is much closer to the real behavior of information searchers than the traditional model of information retrieval is, and, consequently, will guide our thinking better in the design of effective interfaces. Second, the research literature of manual information seeking behavior is drawn on for suggestions of capabilities that users might like to have in online systems. Third, based on the new model and the research on information seeking, suggestions are made for how new search capabilities could be incorporated into the design of search interfaces. Particular attention is given to the nature and types of browsing that can be facilitated.