Abstract
Two recently implemented machine-learning algorithms, RIPPER and sleeping-experts for phrases , are evaluated on a number of large text categorization problems. These algorithms both construct classifiers that allow the “context” of a word w to affect how (or even whether) the presence or absence of w will contribute to a classification. However, RIPPER and sleeping-experts differ radically in many other respects: differences include different notions as to what constitutes a context, different ways of combining contexts to construct a classifier, different methods to search for a combination of contexts, and different criteria as to what contexts should be included in such a combination. In spite of these differences, both RIPPER and sleeping-experts perform extremely well across a wide variety of categorization problems, generally outperforming previously applied learning methods. We view this result as a confirmation of the usefulness of classifiers that represent contextual information.
Keywords
CategorizationComputer scienceArtificial intelligenceConstruct (python library)Machine learningVariety (cybernetics)Classifier (UML)Natural language processingContext (archaeology)
Affiliated Institutions
Related Publications
Machine learning in automated text categorization
The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of ...
Decision combination in multiple classifier systems
A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of ar...
Experiments with a new boosting algorithm
In an earlier paper, we introduced a new "boosting" algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learni...
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
For large, real-world inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the t...
Recognition of handwritten digits by combining independent learning vector quantizations
Classifiers derived by learning vector quantization (LVQ) have well-defined decision regions that can be combined to construct a more accurate classifier. A given point may be i...
Publication Info
- Year
- 1999
- Type
- article
- Volume
- 17
- Issue
- 2
- Pages
- 141-173
- Citations
- 357
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
357
OpenAlex
Cite This
William W. Cohen,
Yoram Singer
(1999).
Context-sensitive learning methods for text categorization.
ACM Transactions on Information Systems
, 17
(2)
, 141-173.
https://doi.org/10.1145/306686.306688
Identifiers
- DOI
- 10.1145/306686.306688