ACL RD-TEC 1.0 Summarization of W04-0845
Paper Title:
SEMANTIC ROLE LABELING WITH BOOSTING, SVMS, MAXIMUM ENTROPY, SNOW, AND DECISION LISTS
SEMANTIC ROLE LABELING WITH BOOSTING, SVMS, MAXIMUM ENTROPY, SNOW, AND DECISION LISTS
Authors: Grace Ngai and Dekai Wu and Marine Carpuat and Chi-Shing Wang and Chi-Yung Wang
Primarily assigned technology terms:
- algorithm
- binary classification
- boosting
- classification
- classification approach
- classifier
- classifier combination
- classifiers
- coding
- combined classifier
- computational linguistics
- decision trees
- kernel
- learning
- learning algorithm
- learning algorithms
- machine learning
- machine learning algorithm
- machine learning algorithms
- machine learning software
- maximum entropy
- maximum entropy model
- maximum entropy system
- nlp
- parser
- polynomial kernel
- pos tagging
- role-labeling
- semantic analysis
- semantic role labeling
- statistical nlp
- support vector machines
- syntactic parser
- tagging
- text classification
- tinysvm software package
- transformation-based learning
- validation
Other assigned terms:
- approach
- array
- association for computational linguistics
- baseline model
- binary classification problem
- classification problem
- classification task
- community
- data set
- entropy
- entropy models
- feature
- feature space
- frame
- frame element
- framenet
- gold standard
- grammatical function
- head word
- heuristics
- implementation
- labeling
- lexical unit
- linguistics
- maps
- maximum entropy models
- method
- methodology
- modifier
- nlp community
- paragraph
- parameter settings
- parse
- parse tree
- part-of-speech
- part-of-speech tag
- phrase
- phrase type
- pos tag
- precision
- role labeling
- semantic
- semantic role
- sentence
- support vector
- svm model
- svms
- syntactic category
- syntactic parse
- target word
- test set
- text
- training
- training data
- training data set
- training set
- transitivity
- tree
- trees
- winnows
- word
