Document Type
Conference Proceeding
Department
Information Systems and Technology (CGU)
Publication Date
2004
Disciplines
Databases and Information Systems | Management Information Systems
Abstract
Current approaches to word sense disambiguation use and combine various machine-learning techniques. Most refer to characteristics of the ambiguous word and surrounding words and are based on hundreds of examples. Unfortunately, developing large training sets is time-consuming. We investigate the use of symbolic knowledge to augment machine-learning techniques for small datasets. UMLS semantic types assigned to concepts found in the sentence and relationships between these semantic types form the knowledge base. A naïve Bayes classifier was trained for 15 words with 100 examples for each. The most frequent sense of a word served as the baseline. The effect of increasingly accurate symbolic knowledge was evaluated in eight experimental conditions. Performance was measured by accuracy based on 10-fold cross-validation. The best condition used only the semantic types of the words in the sentence. Accuracy was then on average 10% higher than the baseline; however, it varied from 8% deterioration to 29% improvement. In a follow-up evaluation, we noted a trend that the best disambiguation was found for words that were the least troublesome to the human evaluators.
Rights Information
© 2004 Gondy Leroy and Thomas C. Rindflesch
Terms of Use & License Information
DOI
10.3233/978-1-60750-949-3-381
Recommended Citation
G. Leroy and T. C. Rindflesch. "Using Symbolic Knowledge in the UMLS to Disambiguate Words in Small Datasets with a Naive Bayes Classifier," MedInfo, San Francisco, 2004.
