ACL RD-TEC 1.0 Summarization of P04-3021
Paper Title:
COMPILING BOOSTEXTER RULES INTO A FINITE-STATE TRANSDUCER
COMPILING BOOSTEXTER RULES INTO A FINITE-STATE TRANSDUCER
Primarily assigned technology terms:
- adaboost
- algorithm
- boosting
- call routing
- classi cation
- classi er
- classication
- decision trees
- decoder
- decoding
- disambiguation
- feature representation
- finite-state transducer
- finitestate
- hidden markov
- hidden markov models
- hmms
- iterative algorithm
- language processing
- learner
- learning
- learning algorithm
- loss function
- machine learning
- markov model
- natural language processing
- nlp
- null processing
- part-of-speech tagging
- processing
- recognition
- recognizer
- search
- sense disambiguation
- speech recognition
- speech recognizer
- tagging
- text classi cation
- training process
- transducer
- transducers
- weak learner
- word sense disambiguation
Other assigned terms:
- alphabet
- ambiguity
- approach
- case
- composition
- context dependency
- context features
- data structure
- document
- estimation
- feature
- hypotheses
- input text
- interpretation
- lattice
- leaf
- mapping
- markov models
- measure
- mechanisms
- named-entity
- natural language
- nlp applications
- nlp tasks
- part-of-speech
- predicates
- procedure
- process
- processing tasks
- regular expressions
- representations
- rewrite rules
- rule sets
- semantic
- sentence
- sentence level
- sentences
- symbols
- syntactic information
- tag sequence
- tag set
- tags
- technique
- text
- training
- training corpus
- training example
- training set
- transcriptions
- transformation
- trees
- vocabulary
- word
- word features
- word level
- word sense
- words
