ACL RD-TEC 1.0 Summarization of W06-2926
Paper Title:
A PIPELINE MODEL FOR BOTTOM-UP DEPENDENCY PARSING
A PIPELINE MODEL FOR BOTTOM-UP DEPENDENCY PARSING
Authors: Ming-Wei Chang and Quang Do and Dan Roth
Primarily assigned technology terms:
- algorithm
- approximation
- bottom-up dependency parsing
- bottom-up parsing
- bottom-up parsing algorithm
- classi cation
- classi er
- computational linguistics
- computational natural language learning
- dependency parsing
- evaluation process
- feature extraction
- kernel
- language learning
- language processing
- learning
- learning algorithm
- learning framework
- machine learning
- mapping algorithm
- multi-class classi cation
- multi-lingual dependency parsing
- natural language learning
- natural language processing
- parser
- parsing
- parsing algorithm
- parsing system
- part-of-speech tagger
- perceptron
- polynomial kernel
- predictor
- processing
- question answering
- scoring
- search
- search algorithm
- semantic role labeling
- shallow parser
- shift-reduce parser
- shift-reduce parsing
- shift-reduce parsing approach
- tagger
Other assigned terms:
- advanced question answering
- annotated corpus
- approach
- association for computational linguistics
- attachment score
- chunk
- classi cation task
- conll-x
- dependency tree
- development set
- experimental setting
- fact
- feature
- feature set
- generation
- index
- intelligence
- knowledge
- labeling
- lemma
- linguistics
- mapping
- measure
- morphological features
- multi-lingual dependency
- natural language
- parse
- parsing approach
- parsing procedure
- part of speech
- part-of-speech
- predicates
- probabilities
- procedure
- process
- projective dependency tree
- relation
- role labeling
- semantic
- semantic role
- sentence
- sentences
- standard deviation
- svms
- system description
- target language
- tokens
- trained model
- training
- training corpus
- training data
- training phase
- tree
- trees
- window size
- word
- word pair
- words
