ACL RD-TEC 1.0 Summarization of I05-3018
Paper Title:
COMBINATION OF MACHINE LEARNING METHODS FOR OPTIMUM CHINESE WORD SEGMENTATION
COMBINATION OF MACHINE LEARNING METHODS FOR OPTIMUM CHINESE WORD SEGMENTATION
Authors: Masayuki Asahara and Kenta Fukuoka and Ai Azuma and Chooi-Ling Goh and Yotaro Watanabe and Yuji Matsumoto and Takashi Tsuzuki
Primarily assigned technology terms:
- algorithm
- character-based chunking
- character-based tagging
- chinese word segmentation
- chunker
- chunking
- classifier
- classifiers
- clustering
- clustering algorithm
- conditional random fields
- decoding
- encoding
- entropy classifier
- extraction method
- hard clustering
- k-means
- k-means clustering
- learning
- learning method
- learning methods
- machine learning
- machine learning methods
- markov model
- matching
- maxent
- maxent classifier
- maximum entropy
- maximum entropy classifier
- maximum matching
- memm-based word segmenter
- normalization
- oov word extraction
- pos tagging
- segmentation
- segmenter
- statistical\/machine learning
- support vector machines
- tagging
- viterbi
- viterbi algorithm
- voting
- word extraction
- word segmentation
- word segmentation bakeoff
- word segmenter
Other assigned terms:
- approach
- bias
- case
- character sequence
- characters
- chinese word
- correlation
- data set
- data sets
- dictionary
- distribution
- entropy
- entropy markov model
- f-measure
- human knowledge
- implementation
- knowledge
- likelihood
- method
- precision
- probabilities
- segmentation bakeoff
- segments
- sentence
- support vector
- tags
- test data
- training
- training data
- training phase
- transition probabilities
- window size
- word
- word candidate
- word classes
- word sequence
- word types
- words
