ACL RD-TEC 1.0 Summarization of W00-1305
Paper Title:
TOPIC ANALYSIS USING A FINITE MIXTURE MODEL
TOPIC ANALYSIS USING A FINITE MIXTURE MODEL
Authors: Hang Li and Kenji Yamanishi
Primarily assigned technology terms:
- algorithm
- analysis method
- classification
- clustering
- clustering algorithm
- em algorithm
- extraction method
- identification
- information extraction
- information retrieval
- key word extraction
- keyword extraction
- learner
- learning
- mining
- model selection
- modeling
- processing
- segmentation
- segmentation method
- statistical modeling
- summarization
- supervised learning
- text classification
- text mining
- text processing
- text segmentation
- text summarization
- texttiling
- topic analysis
- topic identification
- topic spotting
- unsupervised learning
- word clustering
- word co-occurrence calculation
- word extraction
Other assigned terms:
- approach
- case
- cluster
- clusters
- co-occurrence
- coefficient
- content words
- data corpus
- data set
- dictionary
- distribution
- experimental results
- finite mixture model
- identification accuracy
- index
- information theory
- joint distribution
- key words
- keyword
- knowledge
- likelihood
- linear combination
- linguistic
- linguistic theories
- local maximum
- mdl principle
- measure
- measures
- method
- minimum description length
- mixture models
- model selection criterion
- mutual information
- parameter values
- parse
- pre-determined threshold
- precision
- probabilities
- probability
- probability distribution
- probability distributions
- probability model
- probability value
- process
- query
- seed
- seed words
- segmentation accuracy
- sentence
- sentences
- similarity score
- statistics
- term
- terms
- text
- theories
- theory
- topic identification accuracy
- topic structure
- topics
- training
- unlabeled text
- vocabulary
- word
- word co-occurrence
- word distribution
- word frequency
- word sequence
- word types
- words
