ACL RD-TEC 1.0 Summarization of E95-1008
Paper Title:
COLLOCATION MAP FOR OVERCOMING DATA SPARSENESS
COLLOCATION MAP FOR OVERCOMING DATA SPARSENESS
Authors: Moonjoo Kim and Young S. Han and Key-Sun Choi
Primarily assigned technology terms:
- algorithm
- approximation
- bayesian network
- computing
- decision making
- frequency counting
- gibbs sampling
- graph reduction
- hidden markov
- hidden markov models
- hybrid inferencing
- inferencing
- language processing
- measuring
- natural language processing
- neural network
- node reduction
- processing
- sampling
- smoothing
- statistical approaches
- statistical language processing
Other assigned terms:
- bayesian model
- bigram
- case
- collocation
- community
- concept
- conditional distribution
- conditional independence
- conditional probabilities
- data sparseness
- data sparseness problem
- distribution
- empirical results
- estimation
- events
- feature
- implementation
- inferences
- language models
- likelihood
- linguistic
- markov models
- measure
- method
- mutual information
- n-gram
- n-grams
- natural language
- predictive power
- probabilistic information
- probabilistic models
- probabilities
- probability
- probability distribution
- probability distributions
- probability theory
- sigmoid function
- sparseness problem
- statistic
- statistics
- text
- theory
- time complexity
- training
- tree
- word
- words
