ACL RD-TEC 1.0 Summarization of P97-1006
Paper Title:
DOCUMENT CLASSIFICATION USING A FINITE MIXTURE MODEL
DOCUMENT CLASSIFICATION USING A FINITE MIXTURE MODEL
Authors: Hang Li and Kenji Yamanishi
Primarily assigned technology terms:
- algorithm
- bayes estimation
- binary classification
- classification
- clustering
- clustering method
- disambiguation
- document classification
- document retrieval
- em algorithm
- estimation method
- estimator
- hard clustering
- hypothesis testing
- language processing
- latent semantic analysis
- likelihood estimation
- likelihood estimation method
- likelihood estimator
- likelihood ratio test
- maximum likelihood
- maximum likelihood estimation
- maximum likelihood estimator
- natural language processing
- parameter estimation
- processing
- ratio test
- semantic analysis
- sense disambiguation
- soft clustering
- statistical estimation
- statistical hypothesis
- statistical language processing
- word clustering
- word sense disambiguation
Other assigned terms:
- approach
- case
- cluster
- clusters
- data set
- data sets
- data sparseness
- data sparseness problem
- distribution
- document
- estimation
- experimental results
- finite mixture model
- heuristics
- histogram
- hypothesis
- implementation
- intractability
- knowledge
- language processing tasks
- latent semantic
- likelihood
- likelihood ratio
- linear combination
- linguistic
- markov chain
- measure
- method
- mixture models
- natural language
- natural language processing tasks
- notational simplicity
- precision
- prepositions
- probabilistic approach
- probabilities
- probability
- probability distribution
- probability model
- process
- processing tasks
- relative frequency
- reuters corpus
- reuters data set
- semantic
- sparseness problem
- susanne corpus
- target word
- technique
- term
- terms
- test data
- text
- topics
- training
- training data
- vocabulary
- word
- word frequencies
- word sense
- words
