ACL RD-TEC 1.0 Summarization of W98-1307
Paper Title:
LEARNING FINITE-STATE MODELS FOR LANGUAGE UNDERSTANDING
LEARNING FINITE-STATE MODELS FOR LANGUAGE UNDERSTANDING
Authors: David Pico and Enrique Vidal
Primarily assigned technology terms:
- algorithm
- automaton
- beam search
- beam-search
- bootstrapping
- bootstrapping procedure
- categorization
- category identification
- coding
- computing
- database
- decoding
- deterministic parsing
- dynamic programming
- error correcting
- error correction
- finite state
- finite state automaton
- finite state transducers
- finite-state transducers
- finite-state translation
- identification
- inference algorithm
- language translation
- language understanding
- learning
- learning process
- matching
- maximum-likelihood
- modeling
- mutual information estimation
- nl processing
- parsing
- processing
- recognition
- search
- semantic coding
- state automaton
- statistical translation
- subsequential transducer
- tile
- transducer
- transducer inference
- transducers
- transduction
- translation process
- viterbi
- viterbi algorithm
Other assigned terms:
- approach
- beam
- case
- clusters
- community
- concepts
- corpora
- distribution
- error rate
- estimation
- fact
- feature
- formal language
- generation
- input string
- input text
- interpreter
- knowledge
- language model
- large corpus
- learning strategy
- linguistic
- mapping
- meaning
- mechanisms
- method
- mutual information
- natural language
- natural language sentences
- natural-language
- parse
- priori
- probability
- probability distribution
- procedure
- process
- queries
- semantic
- semantic constraints
- semantic space
- sentence
- sentences
- speech input
- symbols
- syntax
- system design
- target language
- technique
- terms
- test set
- text
- training
- training corpus
- training data
- training material
- training set
- translation models
- translation pairs
- translation task
- translations
- tree
- understanding
- vocabulary
- vocabulary size
- word
- word order
- words
