ACL RD-TEC 1.0 Summarization of W00-1310
Paper Title:
NONLOCAL LANGUAGE MODELING BASED ON CONTEXT CO-OCCURRENCE VECTORS
NONLOCAL LANGUAGE MODELING BASED ON CONTEXT CO-OCCURRENCE VECTORS
Authors: Sadao Kurohashi and Manabu Ori
Primarily assigned technology terms:
- approximation
- character recognition
- clustering
- computing
- decomposition
- hierarchical clustering
- language modeling
- latent semaaltic analysis
- learning
- learning method
- linear interpolation
- modeling
- optical character recognition
- pattern recognition
- processing
- re-estimation
- re-estimation procedure
- recognition
- sense dismnbiguation
- singular value decomposition
- speech recognition
- statistical language modeling
- tile
- vector representation
- vector space model
- word sense dismnbiguation
Other assigned terms:
- approach
- bigram
- bigram language model
- bigram model
- cache
- case
- co-occurrence
- co-occurrence information
- co-occurrence matrix
- co-occurrences
- cognitive
- compound noun
- conditional probability
- content words
- context model
- context vector
- contextual information
- cosine similarity
- distribution
- document
- events
- function word
- function words
- interpolation
- language model
- language models
- local context
- measure
- method
- n-gram
- newspaper corpus
- normal distribution
- nouns
- perplexity
- probabilities
- probability
- probability distribution
- probability distributions
- procedure
- process
- pronouns
- queries
- semantic
- semantic representation
- sequence probability
- target vocabulary
- terms
- test set
- text
- tile context
- topics
- training
- training corpus
- trigram
- trigram language model
- trigram model
- unigram
- utterance
- vector space
- vocabulary
- word
- word co-occurrence
- word sense
- word sequence
- word sequence probability
- word sequences
- words
