Communications of the ACM
Computational learning theory: an introduction
Computational learning theory: an introduction
An introduction to computational learning theory
An introduction to computational learning theory
Decomposable modeling in natural language processing
Computational Linguistics
Automatic semantic tagging of unknown proper names
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
| Hi-index | 0.00 |
Word Sense Disambiguation (WSD) is a central task in the area of Natural Language Processing. In the past few years several context-based probabilistic and machine learning methods for WSD have been presented in literature. However, an important area of research that has not been given the attention it deserves is a formal analysis of the parameters affecting the performance of the learning task faced by these systems. Usually performance is estimated by measuring precision and recall of a specific algorithm for specific test sets and environmental conditions. Therefore, a comparison among different learning systems and an objective estimation of the difficulty of the learning task is extremely difficult.In this paper we propose, in the framework of Computational Learning theory, a formal analysis of the relations between accuracy of a context-based WSD system, the complexity of the context representation scheme, and the environmental conditions (e.g. the complexity of language domain and concept inventory).