Optimization of relevance feedback weights
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
NLDB'06 Proceedings of the 11th international conference on Applications of Natural Language to Information Systems
| Hi-index | 0.00 |
This paper presents an algorithm for Word Sense Discrimination that divides the global representation of a word into a number of classes by determining for any two occurrences whether they belong to the same sense or not. We rely on the notion that words that are used in similar contexts will have the same or a closely related meaning, thus, given a target word, we group its dependency co-occurrences in a Word Space Model. Each cluster represents a distinct meaning or sense of that word. We experiment with augmenting the bag of words of each cluster of co-occurrences, the dictionary of sense definition, and augmenting both. Then we count the number of intersections of each word of the bag of clustered senses and the bag of the dictionary of senses following the Lesk method. We find an increase in recall and a decrease in precision when augmenting. However, the best resulting F-measure is for the option of augmenting the both dictionary of senses and the bag of words from the clusters.