The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.

SUMMARIST is an attempt to create a robust automated text summarization system, based on the ‘equation’: summarization = topic identification interpretation generation. Each of these stages contains several independent modules, many of them trained on large corpora of text. We describe the system’s architecture and provide details of some of its modules.

The growing problem of unsolicited bulk e-mail, also known as "spare", has generated a need for reliable anti-spam e-mail filters. Filters of this type have so far been based mostly on manually constructed keyword patterns. An alternative approach has recently been proposed, whereby a Naive Bayesian classifier is trained automatically to detect spam messages. We test this approach on a large collection of personal e-mail messages, which we make publicly available in "encrypted " form contributing towards standard benchmarks. We introduce appropriate cost-sensitive measures, investigating at the same time the effect of attribute-set size, training-corpus size, lemmatization, and stop lists, issues that have not been explored in previous experiments. Finally, the Naive Bayesian filter is compared, in terms of performance, to a filter that uses keyword patterns, and which is part of a widely used e-mail reader. Keywords filtering/routing; text categorization; machine learning and IR; evaluation (general); test collections I.

. In this paper we present a simple linear-time centroid-based document classification algorithm, that despite its simplicity and robust performance, has not been extensively studied and analyzed. Our experiments show that this centroid-based classifier consistently and substantially outperforms other algorithms such as Naive Bayesian, k-nearest-neighbors, and C4.5, on a wide range of datasets. Our analysis shows that the similarity measure used by the centroidbased scheme allows it to classify a new document based on how closely its behavior matches the behavior of the documents belonging to different classes. This matching allows it to dynamically adjust for classes with different densities and accounts for dependencies between the terms in the different classes. 1 Introduction We have seen a tremendous growth in the volume of online text documents available on the Internet, digital libraries, news sources, and company-wide intranets. It has been forecasted that these docu...

this paper, we describe experiments with AutoSlog in two additional domains: joint ventures and microelectronics. We compare the performance of AutoSlog across the three domains, discuss the lessons learned about the generality of this approach, and present results from two experiments which demonstrate that novice users can generate effective dictionaries using AutoSlog. 1 Introduction Portability is a crucial concern for researchers in knowledge-based natural language processing (NLP). Knowledge-based NLP systems typically rely on a conceptual dictionary that has been manually encoded for a specific domain. Although knowledge-based systems have performed well on certain tasks (e.g., [2,4,5,11,16,23]), these systems will not be practical for real world applications until the knowledge that they need can be acquired automatically. Preprint submitted to Elsevier Preprint 21 March We have developed a system called AutoSlog that generates conceptual dictionaries for information extraction automatically. Information extraction (IE) is essentially a form of text skimming, in which specific types of information are extracted from text. There has been a lot of work recently on information extraction in conjunction with the recent message understanding conferences [26--28]. Most information extraction systems rely on a manually encoded dictionary of extraction patterns (e.g., see [12,15,1]). Using AutoSlog, the UMass/MUC-4 system was the first system that could acquire domainspecific extraction patterns automatically [17,18]. In previous work, we showed that AutoSlog could create effective extraction patterns for the domain of terrorism [30]. A dictionary generated by AutoSlog for the terrorism domain achieved 98% of the performance of a handcrafted dictionary that required a...

The past decade has seen a tremendous growth in the amount of experimental and computational biomedical data, specifically in the areas of Genomics and Proteomics. This growth is accompanied by an accelerated increase in the number of biomedical publications discussing the findings. In the last few years there is a lot of interest within the scientific community in literature-mining tools to help sort through this abundance of literature, and find the nuggets of information most relevant and useful for specific analysis tasks. This paper

We investigate the performance of two machine learning algorithms in the context of anti-spam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a need for reliable anti-spam filters. Filters of this type have so far been based mostly on keyword patterns that are constructed by hand and perform poorly. The Naive Bayesian classifier has recently been suggested as an effective method to construct automatically anti-spam filters with superior performance. We investigate thoroughly the performance of the Naive Bayesian filter on a publicly available corpus, contributing towards standard benchmarks. At the same time, we compare the performance of the Naive Bayesian filter to an alternative memory-based learning approach, after introducing suitable cost-sensitive evaluation measures. Both methods achieve very accurate spam filtering, outperforming clearly the keyword-based filter of a widely used e-mail reader.

Most information retrieval systems use stopword lists and stemming algorithms. However, we have found that recognizing singular and plural nouns, verb forms, negation, and prepositions can produce dramatically different text classification results. We present results from text classification experiments that compare relevancy signatures, which use local linguistic context, with corresponding indexing terms that do not. In two different domains, relevancy signatures produced better results than the simple indexing terms. These experiments suggest that stopword lists and stemming algorithms may remove or conflate many words that could be used to create more effective indexing terms. Introduction Most information retrieval systems use a stopword list to prevent common words from being used as indexing terms. Highly frequent words, such as determiners and prepositions, are not considered to be content words because they appear in virtually every document. Stopword lists are almost univer...

Categorization of documents is challenging, as the number of discriminating words can be very large. We present a nearest neighbor classification scheme for text categorization in which the importance of discriminating words is learned using mutual information and weight adjustment techniques. The nearest neighbors for a particular document are then computed based on the matching words and their weights. We evaluate our scheme on both synthetic and real world documents. Our experiments with synthetic data sets show that this scheme is robust under different emulated conditions. Empirical results on real world documents demonstrate that this scheme outperforms state of the art classification algorithms such as C4.5, RIPPER, Rainbow, and PEBLS.

This paper addresses the issue of "algorithm vs. representation" for case-based learning of linguistic knowledge. We first present empirical evidence that the success of case-based learning methods for natural language processing tasks depends to a large degree on the feature set used to describe the training instances. Next, we present a technique for automating feature set selection for case-based learning of linguistic knowledge. Given as input a baseline case representation, the method modifies the representation in response to a number of predefined linguistic biases by adding, deleting, and weighting features appropriately. We apply the linguistic bias approach to feature set selection to the problem of relative pronoun disambiguation and show that the casebased learning agorithm improves as relevant biases are incorporated into the underlying instance representation. Finally, we argue that the linguistic bias approach to feature set selection offers new possibilities for case-based learning of natural language: it simplifies the process of instance representation design and, in theory, obviates the need for separate instance representations for each linguistic knowledge acquisition task. More importantly, the approach offers a mechanism for explicitly combining the frequency information available from corpus-based techniques with linguistic bias information employed in traditional linguistic and knowledge-based approaches to natural language processing.