It has been known that different representations of a query retrieve different sets of documents. Recent work suggests that significant improvements in retrieval performance can be achieved by combining multiple representations of an information need. However, little effort has been made to understand the reason why combining multiple sources of evidence improves retrieval effectiveness. In this paper we analyze why improvements can be achieved with evidence combination, and investigate how evidence should be combined. We describe a rationale for multiple evidence combination, and propose a combining method whose properties coincide with the rationale. We also investigate the effect of using rank instead of similarity on retrieval effectiveness. 1 Introduction A variety of representation techniques for queries and documents have been proposed in the information retrieval (IR) literature, and many corresponding retrieval techniques have also been developed to get higher retrieval effec...

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Three different types of classifiers were investigated in the context of a text categorization problem in the medical domain: the automatic assignment of ICD9 codes to dictated inpatient discharge summaries. K-nearest-neighbor, relevance feedback, and Bayesian independence classifers were applied individually and in combination. A combination of different classifiers produced better results than any single type of classifier. For this specific medical categorization problem, new query formulation and weighting methods used in the k-nearest-neighbor classifier improved performance. 1 Introduction Past research in information retrieval has shown that one can improve retrieval effectiveness by using multiple representations in indexing and query formulation [27] [19] [3] [11] and by using multiple search strategies [5] [24] [7]. In this work, we investigate whether we can attain similar improvements in the domain of text categorization by combining different representations and classif...

Abstract-We report on two studies in the TREC-2 program that investigated the effect on retrieval performance of combination of multiple representations of TREC topics. In one of the projects, five separate Boolean queries for each of the 50 TREC routing topics and 25 of the TREC ad hoc topics were generated by 75 experienced online searchers. Using the INQUERY retrieval system, these queries were both combined into single queries, and used to produce five separate retrieval results for each topic. In the former case, progressive combination of queries led to progressively improving retrieval performance, significantly better than that of single queries, and at least as good as the best individual single-query formulations. In the latter case, data fusion of the ranked lists also led to performance better than that of any single list. In the second project, two automatically produced vector queries and three versions of a manually produced P-norm extended Boolean query for each routing and ad hoc topic were compared and combined. This project investigated six different methods of combination of queries, and the combination of the same queries on different databases. As in the first project, progressive combination led to progressively improving results, with the best results, on average, being achieved by combination through summing of retrieval status values. Both projects found that the best method of combination often led to results that were better than the best performing single query. The combined results from the two projects have also been combined by data fusion. The results of this procedure show that combining evidence from completely different systems also leads to performance improvement. 1.

Frequently a user's information needs are stored in the databases of multiple search engines. It is inconvenient and inefficient for an ordinary user to invoke multiple search engines and identify useful documents from the returned results. To support unified access to multiple search engines, a metasearch engine can be constructed. When a metasearch engine receives a query from a user, it invokes the underlying search engines to retrieve useful information for the user. Metasearch engines have other benefits as a search tool such as increasing the search coverage of the Web and improving the scalability of the search. In this article, we survey techniques that have been proposed to tackle several underlying challenges for building a good metasearch engine. Among the main challenges, the database selection problem is to identify search engines that are likely to return useful documents to a given query. The document selection problem is to determine what documents to retrieve from each identified search engine. The result merging problem is to combine the documents returned from multiple search engines. We will also point out some problems that need to be further researched.

Collection fusion is a data fusion problem in which the re-sults of retrieval runs on separate, autonomous document collections must be merged to produce a single, effective re-sult. This paper explores two collection fusion techniques that learn the rmrnber of documents to retrieve from each collection using only the ranked lists of documents returned in response to past queries and those documents! relevance judgments. Retrieval experiments using the TREC test co]-lection demonstrate that the effectiveness of the fusion tech-niques is within 10’?%of the effectiveness of a run in which the entire set of documents is treated as a single collection. 1

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.

The combination of different text representations and search strategies has become a standard technique for improving the effectiveness of information retrieval. Combination, for example, has been studied extensively in the TREC evaluations and is the basis of the “meta-search” engines used on the Web. This paper examines the development of this technique, including both experimental results and the retrieval models that have been proposed as formal frameworks for combination. We show that combining approaches for information retrieval can be modeled as combining the outputs of multiple classifiers based on one or more representations, and that this simple model can provide explanations for many of the experimental results. We also show that this view of combination is very similar to the inference net model, and that a new approach to retrieval based on language models supports combination and can be integrated with the inference net model.

The heterogeneous Web exacerbates IR problems and short user queries make them worse. The contents of web documents are not enough to find good answer documents. Link information and URL information compensates for the insu #ciencies of content information. However, static combination of multiple evidences may lower the retrieval performance. We need di#erent strategies to find target documents according to a query type. We can classify user queries as three categories, the topic relevance task, the homepage finding task, and the service finding task. In this paper, a user query classification scheme is proposed. This scheme uses the di#erence of distribution, mutual information, the usage rate as anchor texts, and the POS information for the classification. After we classified a user query, we apply di#erent algorithms and information for the better results. For the topic relevance task, we emphasize the content information, on the other hand, for the homepage finding task, we emphasize the Link information and the URL information. We could get the best performance when our proposed classification method with the OKAPI scoring algorithm was used.

In this paper the score distributions of a number of text search engines are modeled. It is shown empirically that the score distributions on a per query basis may be fitted using an exponential distribution for the set of non-relevant documents and a normal distribution for the set of relevant documents. Experiments show that this model fits TREC-3 and TREC-4 data for not only probabilistic search engines like INQUERY but also vector space search engines like SMART for English. We have also used this model to fit the output of other search engines like LSI search engines and search engines indexing other languages like Chinese. It is then shown that given a query for which relevance information is not available, a mixture model consisting of an exponential and a normal distribution can be fitted to the score distribution. These distributions can be used to map the scores of a search engine to probabilities. We also discuss how the shape of the score distributions arise given certain assumptions about word distributions in documents. We hypothesize that all 'good' text search engines operating on any language have similar characteristics. This model has many possible applications. For example, the outputs of different search engines can be combined by averaging the probabilities (optimal if the search engines are independent) or by using the probabilities to select the best engine for each query. Results show that the technique performs as well as the best current combination techniques. This material is based on work supported in part by the National Science Foundation, Library of Congress and Department of Commerce under cooperative agreement number EEC-9209623, in part by the National Science Foundation under grant numbers IRI-9619117 and IIS-9909073, in part by N...