The ability to effectively organize retrieval results becomes more important as the focus of Information Retrieval (IR) shifts towards interactive search processes. Automatic classification techniques are capable of providing the necessary information organization by arranging the retrieved data into groups of documents with common subjects. In this paper, we compare classification methods from IR and Machine Learning (ML) for clustering search results. Issues such as document representation, classification algorithms, and cluster representation are discussed. We introduce several evaluation techniques and use them in preliminary experiments. These experiments indicate that the proposed techniques have promise, but it is clear that user experiments are required to carry out more thorough evaluation.

Abstract. Clustering is a problem of great practical importance in numerous applications. The problem of clustering becomes more challenging when the data is categorical, that is, when there is no inherent distance measure between data values. We introduce LIMBO, a scalable hierarchical categorical clustering algorithm that builds on the Information Bottleneck (IB) framework for quantifying the relevant information preserved when clustering. As a hierarchical algorithm, LIMBO has the advantage that it can produce clusterings of different sizes in a single execution. We use the IB framework to define a distance measure for categorical tuples and we also present a novel distance measure for categorical attribute values. We show how the LIMBO algorithm can be used to cluster both tuples and values. LIMBO handles large data sets by producing a memory bounded summary model for the data. We present an experimental evaluation of LIMBO, and we study how clustering quality compares to other categorical clustering algorithms. LIMBO supports a trade-off between efficiency (in terms of space and time) and quality. We quantify this trade-off and demonstrate that LIMBO allows for substantial improvements in efficiency with negligible decrease in quality. 1

this paper, we describe a semi-automatic wrapper induction system with a powerful wrapper language that helps to capture sophisticated extraction scenarios. The main contributions of our work is the combination of a flexible user interface and algorithmic techniques to minimize the number of interactions required in the training process. We also give preliminary evaluation results

This dissertation examines the role of salience in natural language generation (NLG). The salience of an entity, in intuitive terms, refers to its prominence, and is interpreted as a measure of how well an entity stands out from other entities and biases the preference of the generator in selecting words and complex constructs. Through an analysis of previous work in diverse disciplines, we show the variety of salience effects in NLG. Next, we classify several important determinants of salience, corresponding to different factors contributing to salience. We then delineate two theoretically-significant categories: canonical salience and instantial salience. The former is characterized as a built-in preference in the general conceptual- and linguistic knowledge of the speaker. The latter refers to the salience of specific objects in the context of NLG, and may accrue through such determinants as vividness and recency of mention. Psycholinguistic results of Osgood and Bock are highligh...

This paper presents a new Similarity Based Agglomerative Clustering(SBAC) algorithm that works well for data with mixed numeric and nominal features. A similarity measure, proposed by Goodall for biological taxonomy[13], that gives greater weight to uncommon feature-value matches in similarity computations and makes no assumptions of the underlying distributions of the feature-values, is adopted to define the similarity measure between pairs of objects. An agglomerative algorithm is employed to construct a concept tree, and a simple distinctness heuristic is used to extract a partition of the data. The performance of SBAC has been studied on artificially generated data sets. Results demonstrate the effectiveness of this algorithm in unsupervised discovery tasks. Comparisons with other schemes illustrate the superior performance of the algorithm. 1 Introduction The widespread use of computers and information technology has made extensive data collection in businesses, manufacturing, an...

. The ability to effectively organize retrieval results becomes more important as the focus of Information Retrieval (IR) shifts towards interactive search processes. Automatic classification techniques are capable of providing the necessary information organization by arranging the retrieved data into groups of documents with common subjects. In this paper, we compare classification methods from IR and Machine Learning (ML) for clustering search results. Issues such as document representation, classification algorithms, and cluster representation are discussed. We introduce several evaluation techniques and use them in preliminary experiments. These experiments indicate that the proposed techniques have promise, but it is clear that user experiments are required to carry out more thorough evaluation. T his 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. Any opinions, findings and conclusions or recommendations expressed in this material are the author(s) and do not necessarily reflect those of the sponsor. This material is based on work supported in part by NRaD Contract Number N66001-94-D-6054. 2 1