ACL RD-TEC 1.0 Summarization of J94-2001
Paper Title:
TAGGING ENGLISH TEXT WITH A PROBABILISTIC MODEL
TAGGING ENGLISH TEXT WITH A PROBABILISTIC MODEL
Primarily assigned technology terms:
- algorithm
- baum-welch algorithm
- computational linguistics
- computing
- continuous speech recognition
- dynamic programming
- dynamic programming scheme
- hidden markov
- hidden markov model
- hidden markov models
- ibm speech recognition
- interpolation algorithm
- iterative procedure
- likelihood training
- markov model
- maximum likelihood
- maximum likelihood training
- ml training
- morphological analysis
- neural networks
- processing
- reasoning
- recognition
- signal processing
- smoothing
- speech recognition
- tagging
- tagging procedure
- viterbi
- viterbi algorithm
Other assigned terms:
- approach
- association for computational linguistics
- case
- coefficient
- computational complexity
- computer research
- continuous speech
- dictionary
- distribution
- english language
- english text
- error rate
- events
- fact
- frequency counts
- intention
- interpolation
- interpretation
- likelihood
- linguistic
- linguistic meaning
- linguistics
- local context
- markov models
- mathematical expression
- meaning
- measure
- method
- parse
- part of speech
- perplexity
- phonemes
- probabilistic model
- probabilistic models
- probabilities
- probability
- probability distributions
- procedure
- process
- relative frequency
- sentence
- sentences
- signal
- speech signal
- tagged text
- tagging accuracy
- tags
- test data
- text
- theoretical framework
- training
- training data
- training material
- training text
- transition probabilities
- treebank
- untagged text
- user
- vocabulary
- word
- word level
- word sequence
- word sequences
- words
