Amanote Research

Amanote Research

    RegisterSign In

Unsupervised Learning of Word Sense Disambiguation Rules by Estimating an Optimum Iteration Number in the EM Algorithm

doi 10.3115/1119176.1119182
Full Text
Open PDF
Abstract

Available in full text

Date

January 1, 2003

Authors
Hiroyuki ShinnouMinoru Sasaki
Publisher

Association for Computational Linguistics


Related search

ShotgunWSD: An Unsupervised Algorithm for Global Word Sense Disambiguation Inspired by DNA Sequencing

2017English

Unsupervised Domain Adaptations for Word Sense Disambiguation by Learning Under Covariate Shift

Journal of Natural Language Processing
2014English

Harmony Search Algorithm for Word Sense Disambiguation

PLoS ONE
Multidisciplinary
2015English

Review: Semi-Supervised Learning Methods for Word Sense Disambiguation

IOSR Journal of Computer Engineering
2013English

Error Driven Word Sense Disambiguation

1998English

Adapted Lesk Algorithm Based Word Sense Disambiguation Using the Context Information

International Journal of Advanced Computer Science and Applications
Computer Science
2020English

Estimating Upper and Lower Bounds on the Performance of Word-Sense Disambiguation Programs

1992English

An Empirical Evaluation of Knowledge Sources and Learning Algorithms for Word Sense Disambiguation

2002English

Word Sense Disambiguation Using Label Propagation Based Semi-Supervised Learning

2005English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2025 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy