ACL RD-TEC 1.0 Summarization of W04-0857
Paper Title:
GENERATIVE MODELS FOR SEMANTIC ROLE LABELING
GENERATIVE MODELS FOR SEMANTIC ROLE LABELING
Authors: Cynthia Thompson and Siddharth Patwardhan and Carolin Arnold
Primarily assigned technology terms:
- algorithm
- bayes classifier
- chunking
- classification
- classifier
- classifiers
- computational linguistics
- cross-validation
- greedy search
- grouping
- hidden markov
- hidden markov model
- labeler
- language generation
- learning
- learning approach
- learning techniques
- likelihood estimate
- machine learning
- machine learning approach
- machine learning techniques
- machine translation
- markov model
- maximum likelihood
- naive bayes
- naive bayes classifier
- natural language generation
- parser
- role-labeling
- scoring
- search
- semantic analysis
- semantic role labeling
- semi-supervised learning
- smoothing
- statistical parser
- top-down approach
- validation
- viterbi
- viterbi algorithm
- weka
Other assigned terms:
- approach
- association for computational linguistics
- bias
- case
- computational complexity
- data set
- distribution
- fact
- feature
- feature set
- frame
- frame element
- framenet
- generation
- generative model
- generative models
- head word
- heuristic
- joint probability
- joint probability distribution
- labeled training data
- labeling
- likelihood
- linguistics
- maximum likelihood estimate
- method
- model parameters
- natural language
- natural language sentence
- parse
- parse tree
- phrase
- phrase type
- precision
- preposition
- probabilities
- probability
- probability distribution
- process
- role labeling
- root node
- search space
- semantic
- semantic role
- semantic roles
- sentence
- sentences
- source language
- subtree
- syntactic parse
- target language
- target word
- technique
- text
- toolkit
- training
- training corpus
- training data
- training example
- training examples
- training time
- tree
- trees
- word
- word count
- words
