ACL RD-TEC 1.0 Summarization of P06-2109
Paper Title:
TRIMMING CFG PARSE TREES FOR SENTENCE COMPRESSION USING MACHINE LEARNING APPROACHES
TRIMMING CFG PARSE TREES FOR SENTENCE COMPRESSION USING MACHINE LEARNING APPROACHES
Authors: Yuya Unno and Takashi Ninomiya and Yusuke Miyao and Jun'ichi Tsujii
Primarily assigned technology terms:
- algorithm
- learning
- learning approach
- learning approaches
- machine learning
- machine learning approach
- machine learning approaches
- machine translation
- matching
- maximum entropy
- maximum entropy method
- maximum entropy model
- maximum-entropy
- noisy-channel model
- parser
- sentence compression
- summarization
- unsupervised learning
Other assigned terms:
- adverb
- approach
- bigram
- bleu
- bleu score
- bleu scores
- complex sentence
- compression ratio
- context-free grammar
- contextual information
- corpora
- corpus size
- decision-based model
- development set
- distribution
- entropy
- evaluation method
- events
- experimental results
- f-measure
- feature
- forest
- generation
- grammar
- grammaticality
- human judgments
- joint probability
- joint probability distribution
- language models
- lexical features
- machine translation quality
- meaning
- meanings
- measure
- measures
- method
- n-gram
- parallel corpus
- parse
- parse tree
- pcfg
- phrase
- probabilistic model
- probabilities
- probability
- probability distribution
- process
- relation
- root node
- semantic
- sentence
- sentence compression problem
- sentences
- statistics
- subtree
- subtrees
- syntactic trees
- terminals
- terms
- test corpus
- training
- training corpus
- training data
- transformation
- transformation rule
- translation quality
- tree
- tree node
- tree structure
- tree structures
- trees
- uniform distribution
- unigram
- user
- word
- words
- ziff-davis corpus
