Can machines learn to categorize text with human-like understanding? This paper investigates the effectiveness of context-sensitive learning methods in large text categorization problems. It evaluates two algorithms, RIPPER and sleeping-experts for phrases, which construct classifiers that consider the context of a word to influence its contribution to classification. While RIPPER and sleeping-experts differ in their approach to defining and combining contexts, they both demonstrate exceptional performance across a wide range of categorization tasks. The algorithms outperform previously applied learning methods, underscoring the value of incorporating contextual information. These results highlight the significance of context in machine learning and natural language processing. The findings have broad implications for improving the accuracy and efficiency of text categorization in various applications, from information retrieval to sentiment analysis and automated content tagging.
This article is highly relevant to ACM Transactions on Information Systems, which focuses on the design, development, and evaluation of information systems. By presenting and comparing two novel machine-learning algorithms for text categorization, the paper addresses a key challenge in information retrieval and knowledge management, aligning with the journal’s emphasis on innovative approaches to information processing.