We explore how to organize a text database hierarchically to aid better searching and browsing. We propose to exploit the natural hierarchy of topics, or taxonomy, that many corpora,suchas internet directories, digital libraries, and patent databases enjoy. In our system, the user navigates through the query response not as a at unstructured list, but embedded in the familiar taxonomy, and annotated with document signatures computed dynamically with respect to where the user is located at any time. Weshowhowto update such databases with new documents with high speed and accuracy. Weuse techniques from statistical pattern recognition to e ciently separate the feature words or discriminants from the noise words at each node of the taxonomy. Using these, we build a multi-level classi er. At each node, this classi er can ignore the large number of noise words in a document. Thus the classi er has a small model size and is very fast. However, owing to the use of context-sensitive features, the classi er is very accurate. We report on experiences with the Reuters newswire benchmark, the US Patent database, and web document samples from Yahoo!. 1

With over 800 million pages covering most areas of human endeavor, the World-wide Web is a fertile ground for data mining research to make a difference to the effectiveness of information search. Today, Web surfers access the Web through two dominant interfaces: clicking on hyperlinks and searching via keyword queries. This process is often tentative and unsatisfactory. Better support is needed for expressing one's information need and dealing with a search result in more structured ways than available now. Data mining and machine learning have significant roles to play towards this end. In this paper we will survey recent advances in learning and mining problems related to hypertext in general and the Web in particular. We will review the continuum of supervised to semi-supervised to unsupervised learning problems, highlight the specific challenges which distinguish data mining in the hypertext domain from data mining in the context of data warehouses, and summarize the key areas of ...

Building models of language is a central task in natural language processing. Traditionally, language has been modeled with manually-constructed grammars that describe which strings are grammatical and which are not; however, with the recent availability of massive amounts of on-line text, statistically-trained models are an attractive alternative. These models are generally probabilistic, yielding a score reflecting sentence frequency instead of a binary grammaticality judgement. Probabilistic models of language are a fundamental tool in speech recognition for resolving acoustically ambiguous utterances. For example, we prefer the transcription forbear to four bear as the former string is far more frequent in English text. Probabilistic models also have application in optical character recognition, handwriting recognition, spelling correction, part-of-speech tagging, and machine translation. In this thesis, we investigate three problems involving the probabilistic modeling of languag...

We present a principled methodology for filtering news stories by formal measures of information novelty, and show how the techniques can be used to custom-tailor newsfeeds based on information that a user has already reviewed. We review methods for analyzing novelty and then describe Newsjunkie, a system that personalizes news for users by identifying the novelty of stories in the context of stories they have already reviewed. Newsjunkie employs novelty-analysis algorithms that represent articles as words and named entities. The algorithms analyze inter- and intra- document dynamics by considering how information evolves over time from article to article, as well as within individual articles. We review the results of a user study undertaken to gauge the value of the approach over legacy time-based review of newsfeeds, and also to compare the performance of alternate distance metrics that are used to estimate the dissimilarity between candidate new articles and sets of previously reviewed articles.

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In this paper we examine the problem of estimating the parameters of a multinomial distribution over a large number of discrete outcomes, most of which do not appear in the training data. We analyze this problem from a Bayesian perspective and develop a hierarchical prior that incorporates the assumption that the observed outcomes constitute only a small subset of the possible outcomes. We show how to efficiently perform exact inference with this form of hierarchical prior and compare our method to standard approaches and demonstrate its merits. Category: Algorithms and Architectures Presentation preference: none This paper was not submitted elsewhere nor will be submitted during NIPS review period. 1 Introduction One of the most important problems in statistical inference is multinomialestimation: Given a past history of observations independent trials with a discrete set of outcomes, predict the probability of the next trial. Such estimators are the basic building blocks in mor...

Abstract. We describe Athena: a system for creating, exploiting, and maintaining a hierarchy of textual documents through interactive miningbased operations. Requirements of any such system include speed and minimal end-user e ort. Athena satis es these requirements through linear-time classi cation and clustering engines which are applied interactively to speed the development of accurate models. Naive Bayes classi ers are recognized to be among the best for classifying text. We show that our specialization of the Naive Bayes classi er is considerably more accurate (7 to 29 % absolute increase in accuracy) than a standard implementation. Our enhancements include using Lidstone's law of succession instead of Laplace's law, under-weighting long documents, and over-weighting author and subject. We also present a new interactive clustering algorithm, C-Evolve, for topic discovery. C-Evolve rst nds highly accurate cluster digests (partial clusters), gets user feedback to merge and correct these digests, and then uses the classi cation algorithm to complete the partitioning of the data. By allowing this interactivity in the clustering process, C-Evolve achieves considerably higher clustering accuracy (10 to 20 % absolute increase in our experiments) than the popular K-Means and agglomerative clustering methods. 1

Hard disk drive failures are rare but are often costly. The ability to predict failures is important to consumers, drive manufacturers, and computer system manufacturers alike. In this paper we investigate the abilities of two Bayesian methods to predict disk drive failures based on measurements of drive internal conditions. We first view the problem from an anomaly detection stance. We introduce a mixture model of naive Bayes submodels (i.e. clusters) that is trained using expectation-maximization. The second method is a naive Bayes classifier, a supervised learning approach. Both methods are tested on realworld data concerning 1936 drives. The predictive accuracy of both algorithms is far higher than the accuracy of thresholding methods used in the disk drive industry today. 1.

Classification is a well-established operation in text mining. Given a set of labels A and a set DA of training documents tagged with these labels, a classifier learns to assign labels to unlabeled test documents. Suppose we also had available a di#erent set of labels B, together with a set of documents DB marked with labels from B. If A and B have some semantic overlap, can the availability of DB help us build a better classifier for A, and vice versa? We answer this question in the a#rmative by proposing cross-training : a new approach to semi-supervised learning in presence of multiple label sets. We give distributional and discriminative algorithms for cross-training and show, through extensive experiments, that cross-training can discover and exploit probabilistic relations between two taxonomies for more accurate classification.

In this paper I present a novel Machine Learning approach to the acquisition of stochastic string transductions based on Pair Hidden Markov Models (PHMMs), a model used in computational biology. I show how these models can be used to learn morphological processes in a variety of languages, including English, German and Arabic. Previous techniques for learning morphology have been restricted to languages with essentially concatenative morphology.