Semi-supervised Learning for Word Sense Disambiguation
Abstract
This work is a study of the impact of multiple aspects in a classic unsupervised word sense disambiguation algorithm. We identify relevant factors in a decision rule algorithm, including the initial labeling of examples, the formalization of the rule confidence, and the criteria for accepting a decision rule. Some of these factors are only implicitly considered in the original literature. We then propose a lightly supervised version of the algorithm, and employ a pseudo-word-based strategy to evaluate the impact of these factors. The obtained performances are comparable with those of highly optimized formulations of the word sense disambiguation method.
- Publication:
-
arXiv e-prints
- Pub Date:
- August 2019
- DOI:
- 10.48550/arXiv.1908.09641
- arXiv:
- arXiv:1908.09641
- Bibcode:
- 2019arXiv190809641G
- Keywords:
-
- Computer Science - Computation and Language;
- Computer Science - Artificial Intelligence;
- Computer Science - Machine Learning
- E-Print:
- This work was awarded the Third Place in the EST 2013 Contest (ISSN 1850-2946) at the 42nd JAIIO (Annals of 42nd JAIIO - Argentine Journals of Informatics - ISSN 1850-2776)