Semi-supervised Learning for Word Sense Disambiguation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Word Sense DisambiguationSimilar Papers 제목 키워드 기반
Semi-Supervised Word Sense Disambiguation Using Word Embeddings in General and Specific Domains
Semi-supervised Word Sense Disambiguation with Neural Models
Determining the intended sense of words in text - word sense disambiguation (WSD) - is a long standing problem in natural language processing. Recently, researchers have shown promising results using word vectors extract…
Language ModelingLanguage ModellingWord Sense DisambiguationA Semi-Supervised Method for Arabic Word Sense Disambiguation Using a Weighted Directed Graph
Semi-supervised Word Sense Disambiguation Using Example Similarity Graph
Word Sense Disambiguation (WSD) is a well-known problem in the natural language processing. In recent years, there has been increasing interest in applying neural net-works and machine learning techniques to solve WSD pr…
Word Sense DisambiguationPoKED: A Semi-Supervised System for Word Sense Disambiguation
Word Sense Disambiguation (WSD) is an open problem in Natural Language Processing, which is challenging and useful in both supervised and unsupervised settings where all the words in any given text need to be disambiguat…
Language ModelingLanguage ModellingPositionSentence+1