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.

The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classification, focusing on the distributional assump- tions made about word occurrences in documents.

This paper examines the use of inductive learning to categorize natural language documents into predefined content categories. Categorization of text is of increasing importance in information retrieval and natural language processing systems. Previous research on automated text categorization has mixed machine learning and knowledge engineering methods, making it difficult to draw conclusions about the performance of particular methods. In this paper we present empirical results on the performance of a Bayesian classifier and a decision tree learning algorithm on two text categorization data sets. We find that both algorithms achieve reasonable performance and allow controlled tradeoffs between false positives and false negatives. The stepwise feature selection in the decision tree algorithm is particularly effective in dealing with the large feature sets common in text categorization. However, even this algorithm is aided by an initial prefiltering of features, confirming the results...

. We discuss algorithms for learning and revising user profiles that can determine which World Wide Web sites on a given topic would be interesting to a user. We describe the use of a naive Bayesian classifier for this task, and demonstrate that it can incrementally learn profiles from user feedback on the interestingness of Web sites. Furthermore, the Bayesian classifier may easily be extended to revise user provided profiles. In an experimental evaluation we compare the Bayesian classifier to computationally more intensive alternatives, and show that it performs at least as well as these approaches throughout a range of different domains. In addition, we empirically analyze the effects of providing the classifier with background knowledge in form of user defined profiles and examine the use of lexical knowledge for feature selection. We find that both approaches can substantially increase the prediction accuracy. Keywords: Information filtering, intelligent agents, multistrategy lea...

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...

this article. For the purpose of text classification, the answer keys serve only as a set of correct classifications for each text. If a text has instantiated key templates associated with it in the corpus, then it should be classified as a relevant text. If a text has no instantiated key templates associated with it (i.e., only a dummy template) then it should be classified as an irrelevant text. This is a binary classification problem: a text is either relevant to the terrorism domain or irrelevant. The texts were selected by keyword search from a database of newswire articles 2 because they contained words associated with terrorism. However, many of them did not mention any relevant terrorist incidents. Of the 1700 texts in the MUC4 corpus, only 53% described a relevant terrorist event. Because many of the texts in the corpus were irrelevant, the MUC-4 systems had to distinguish the relevant from the irrelevant texts. Although the MUC-4 task was information extraction, information detection 4 (i.e, text classification) was an implicit subtask. To be successful in MUC-4, the information extraction systems also had to be good at detection. Our MUC-4 system did not use a separate text classification module. Instead, we extracted information from every text and relied on a discourse analysis module to discard irrelevant templates. This strategy was very effective, 5 but it was expensive. A reliable text classification module could have filtered out irrele- 1MUC-3 was the Third Message Understanding ConferenCe held in 1991 [MUC-3 Proceedings 19911

Most work on intelligent information agents has thus far focused on systems that are accessible through the World Wide Web. As demanding schedules prohibit people from continuous access to their computers, there is a clear demand for information systems that do not require workstation access or graphical user interfaces. We present a personal news agent that is designed to become part of an intelligent, IP-enabled radio, which uses synthesized speech to read news stories to a user. Based on voice feedback from the user, the system automatically adapts to the user's preferences and interests. In addition to time-coded feedback, we explore two components of the system that facilitate the automated induction of accurate interest profiles. First, we motivate the use of a multistrategy machine learning approach that allows for the induction of user models that consist of separate models for long-term and short-term interests. Second, we investigate the use of "concept feedback", a novel fo...

Information extraction (IE) is the problem of filling out pre-defined structured summaries from text documents. We are interested in performing IE in non-traditional domains, where much of the text is often ungrammatical, such as electronic bulletin board posts and Web pages. We suggest that the best approach is one that takes into account many different kinds of information, and argue for the suitability of a multistrategy approach. We describe learners for IE drawn from three separate machine learning paradigms: rote memorization, term-space text classification, and relational rule induction. By building regression models mapping from learner confidence to probability of correctness and combining probabilities appropriately, it is possible to improve extraction accuracy over that achieved by any individual learner. We describe three different multistrategy approaches. Experiments on two IE domains, a collection of electronic seminar announcements from a university computer science de...

While certain standard procedures are widely used for evaluating text retrieval systems and algorithms, the same is not true for text categorization. Omission of important data from reports is common and methods of measuring effectiveness vary widely. This has made judging the relative merits of techniques for text categorization difficult and has disguised important research issues. In this paper I discuss a variety of ways of evaluating the effectiveness of text categorization systems, drawing both on reported categorization experiments and on methods used in evaluating query-driven retrieval. I also consider the extent to which the same evaluation methods may be used with systems for text extraction, a more complex task. In evaluating either kind of system, the purpose for which the output is to be used is crucial in choosing appropriate evaluation methods. INTRODUCTION Text classification systems, i.e. systems which can make distinctions between meaningful classes of texts, have ...

The commas are the most useful and usable of all the stops. It is highly important to put them in place as you go along. If you try to come back after doing a paragraph and stick them in the various spots that tempt you you will discover that they tend to swarm like minnows into all sorts of crevices whose existence you hadn’t realized and before you know it the whole long sentence becomes immobilized and lashed up squirming in commas. Better to use them sparingly, and with affection precisely when the need for one arises, nicely, by itself.