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...

The effect of selecting varying numbers and kinds of features for use in predicting category membership was investigated on the Reuters and MUC-3 text categorization data sets. Good categorization performance was achieved using a statistical classifier and a proportional assignment strategy. The optimal feature set size for word-based indexing was found to be surprisingly low (10 to 15 features) despite the large training sets. The extraction of new text features by syntactic analysis and feature clustering was investigated on the Reuters data set. Syntactic indexing phrases, clusters of these phrases, and clusters of words were all found to provide less effective representations than individual words. 1. Introduction Text categorization---the automated assigning of natural language texts to predefined categories based on their content---is a task of increasing importance. Its applications include indexing texts to support document retrieval [1], extracting data from texts [2], and ai...

This paper describes and analyzes the results of the Third Message Understanding Conference (MUC-3). It reviews the purpose, history, and methodology of the conference, summarizes the participating systems, discusses issues of measuring system effectiveness, describes the linguistic phenomena tests, and provides a critical look at the evaluation in terms of the lessons learned. One of the common problems with evaluations is that the statistical significance of the results is unknown. In the discussion of system performance, the statistical significance of the evaluation results is reported and the use of approximate randomization to calculate the statistical significance of the results of MUC-3 is described

We present in this paper a combination of Machine Learning based Information Retrieval (IR) techniques and stochastic language modelling in a hierarchical system that extracts surface information from text. At the lowest level of this hierarchy, documents and paragraphs are successively routed with IR techniques. At the top level, a stochastic language model extracts the most relevant phrases, and labels the type of information they contain. The approach and preliminary results are demonstrated on a subset of the MUC-6 Scenario Templates task.

AIR/X is a rule-based system for automatic indexing with a controlled vocabulary. The indexing process consists of several stages, with specific rule bases involved in each stage. Most of these rule bases are constructed automatically, especially the large number of term-descriptor rules. We describe the different stages and the overall architecture of the system. Then we present a specific application, the AIR/PHYS system developed for a large physics database. We illustrate the system by giving a detailed example and present experimental results for different system parameter settings. 1 Introduction The AIR/X system described in this paper performs an automatic indexing with index terms (called descriptors here) from a controlled vocabulary. The texts to be indexed are abstracts written in English. The indexing process consists of several stages, with specific rule bases involved in each stage. In order to cope with large subject fields, appropriate rule bases have to be developed....

ABSTRACT: In this paper, firstly we report experimental results on applying information extraction (IE) methodology to the task of summarizing clinical trial design information in focus on “Compared Treatment”, “Endpoint ” and “Patient Population ” from clinical trial MEDLINE abstracts. From these results, we have come to see this problem as one that can be decomposed into a sentence classification subtask and an IE subtask. By classifying sentences from clinical trial abstracts and only performing IE on sentences that are most likely to contain relevant information, we hypothesize that the accuracy of information extracted from the abstracts can be increased. As preparation for testing this theory in the next stage, we conducted an experiment applying state-of-the-art sentence classification techniques to the clinical trial abstracts and evaluated its potential in

We present in this paper a combination of Machine Learning based Information Retrieval (IR) techniques and stochastic language modelling in a hierarchical system that extracts surface information from text. At the lowest level of this hierarchy, documents and paragraphs are successively routed with IR techniques. At the top level, a stochastic language model extracts the most relevant phrases, and labels the type of information they contain. The approach and preliminary results are demonstrated on a subset of the MUC-6 Scenario Templates task. 1 Introduction The extraction of information in textual data, today, relies mainly on linguistic analysis. Successful systems have been demonstrated for many information extraction problems [7]. These systems are usually complex and expensive to build, aimed at limited domains of knowledge and difficult to extend. In order to overcome these problems Information Extraction (IE) systems recently began to integrate machine learning (ML) techniques ...