ACL RD-TEC 1.0 Summarization of W03-0406
Paper Title:
UNSUPERVISED LEARNING OF WORD SENSE DISAMBIGUATION RULES BY ESTIMATING AN OPTIMUM ITERATION NUMBER IN THE EM ALGORITHM
UNSUPERVISED LEARNING OF WORD SENSE DISAMBIGUATION RULES BY ESTIMATING AN OPTIMUM ITERATION NUMBER IN THE EM ALGORITHM
Authors: Hiroyuki Shinnou and Minoru Sasaki
Primarily assigned technology terms:
- adaboost
- algorithm
- classification
- classifier
- co-training
- cross validation
- decision trees
- disambiguation
- em algorithm
- em method
- estimation method
- expectation-maximization
- inductive learning
- language processing
- learning
- learning method
- learning methods
- morphological analysis
- multiclass classification
- naive bayes
- natural language processing
- processing
- scoring
- sense disambiguation
- supervised learning
- syntactic analysis
- text classification
- unsupervised learning
- unsupervised learning method
- validation
- word sense disambiguation
Other assigned terms:
- case
- characters
- classification problem
- classification tasks
- content words
- convergence
- correlation
- dictionary
- discourse
- distribution
- estimation
- feature
- feature sets
- heuristic
- heuristics
- interpretation
- kullback-leibler divergence
- meaning
- meanings
- method
- natural language
- nouns
- part of speech
- precision
- probability
- procedure
- relation
- sentence
- sentences
- slot
- target word
- test data
- text
- theorem
- thesaurus
- trees
- verb
- word
- word sense
- words
