ACL RD-TEC 1.0 Summarization of W06-3207
Paper Title:
RICHNESS OF THE BASE AND PROBABILISTIC UNSUPERVISED LEARNING IN OPTIMALITY THEORY
RICHNESS OF THE BASE AND PROBABILISTIC UNSUPERVISED LEARNING IN OPTIMALITY THEORY
Primarily assigned technology terms:
- algorithm
- encoding
- expectation maximization
- expectation maximization algorithm
- expectation-maximization
- expectation-maximization algorithm
- final state
- grammar learning
- human language
- human language acquisition
- identification
- instantiation
- language acquisition
- language learning
- learner
- learning
- learning algorithm
- likelihood maximization
- maximization algorithm
- modeling
- optimization
- probabilistic modeling
- ranking
- ranking algorithm
- re-estimation
- search
- unsupervised algorithm
- unsupervised learning
- unsupervised learning algorithm
Other assigned terms:
- approach
- artificial language
- bias
- case
- child language acquisition
- complex word
- conditional probabilities
- conditional probability
- constraint grammar
- constraint violation
- data set
- distribution
- fact
- grammar
- grammars
- heuristic
- hypotheses
- hypothesis
- joint distribution
- joint probability
- knowledge
- language data
- learnability
- learning problem
- lexical item
- lexical items
- lexical stress
- lexicon
- likelihood
- linguistic
- linguistics
- linguistics research
- mapping
- mappings
- morpheme
- morphemes
- natural languages
- noise
- optimality theoretic grammars
- optimality theory
- parameter settings
- parameter space
- probabilistic lexicon
- probabilistic model
- probabilities
- probability
- probability distribution
- probability distributions
- probability model
- procedure
- proposition
- search procedure
- search space
- segments
- stress
- stress pattern
- structural description
- suffixes
- surface form
- test set
- theory
- training
- training data
- uniform probability
- vowel
- word
- words
