Work

Co-training and Self-training for Word Sense Disambiguation

Year: 2004

Type: conference-paper

Abstract: This paper investigates the application of co-training and self-training to word sense disambiguation. Optimal and empirical parameter selection methods for co-training and self-training are investigated, with various degrees of error reduction. A new method that combines co-training with majority voting is introduced, with the effect of smoothing the bootstrapping learning curves, and improving the average performance.

Source: University of North Texas Digital Library (University of North Texas)

Author: Rada Mihalcea

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Award: SGER: Exploratory Research of Word Sense Disambiguation Methods for All Words in Open Text

OpenAlex ID: W1885010341

FWCI: 4.752

Cites: 9

Cited by: 171

Related to: 10