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Volumn , Issue , 2005, Pages 134-137

Combination of machine learning methods for optimum Chinese word segmentation

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; IMAGE SEGMENTATION; MARKOV PROCESSES; MAXIMUM ENTROPY METHODS; SUPPORT VECTOR MACHINES;

EID: 38049057103     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (22)

References (10)
  • 2
    • 74349108680 scopus 로고    scopus 로고
    • Pruning False Unknown Words to Improve Chinese Word Segmentation
    • b pages
    • Chooi-Ling Goh, Masayuki Asahara, and Yuji Matsumoto. 2004b. Pruning False Unknown Words to Improve Chinese Word Segmentation. In Proc. of PACLIC-18, pages 139–149.
    • (2004) Proc. of PACLIC-18 , pp. 139-149
    • Goh, Chooi-Ling1    Asahara, Masayuki2    Matsumoto, Yuji3
  • 3
    • 80053222535 scopus 로고    scopus 로고
    • Chunking with Support Vector Machines
    • Taku Kudo and Yuji Matsumoto. 2001. Chunking with Support Vector Machines. In Proc. of NAACL-2001, pages 192–199.
    • (2001) Proc. of NAACL-2001 , pp. 192-199
    • Kudo, Taku1    Matsumoto, Yuji2
  • 4
    • 85111262831 scopus 로고    scopus 로고
    • Applying Conditional Random Fields to Japanese Morphological Analysis
    • Taku Kudo and Yuji Matsumoto. 2004. Applying Conditional Random Fields to Japanese Morphological Analysis. In Proc. of EMNLP-2004, pages 230–237.
    • (2004) Proc. of EMNLP-2004 , pp. 230-237
    • Kudo, Taku1    Matsumoto, Yuji2
  • 5
    • 0142192295 scopus 로고    scopus 로고
    • Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
    • John Lafferty, Andrew McCallum, and Fernando Pereira. 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proc. of ICML-2001, pages 282–289.
    • (2001) Proc. of ICML-2001 , pp. 282-289
    • Lafferty, John1    McCallum, Andrew2    Pereira, Fernando3
  • 6
    • 0000747663 scopus 로고    scopus 로고
    • Maximum Entropy Markov Models for Information Extraction and Segmentation
    • Andrew McCallum, Dayne Freitag, and Fernando Pereira. 2000. Maximum Entropy Markov Models for Information Extraction and Segmentation. In Proc. of ICML-2000, pages 591–598.
    • (2000) Proc. of ICML-2000 , pp. 591-598
    • McCallum, Andrew1    Freitag, Dayne2    Pereira, Fernando3
  • 7
    • 85119101524 scopus 로고    scopus 로고
    • Chinese and Japanese Word Segmentation Using Word-Level and Character-Level Information
    • Tetsuji Nakagawa. 2004. Chinese and Japanese Word Segmentation Using Word-Level and Character-Level Information. In Proc. of COLING-2004, pages 466–472.
    • (2004) Proc. of COLING-2004 , pp. 466-472
    • Nakagawa, Tetsuji1
  • 8
    • 85116342676 scopus 로고    scopus 로고
    • Chinese Segmentation and New Word Detection using Conditional Random Fields
    • Fuchun Peng and Andrew McCallum. 2004. Chinese Segmentation and New Word Detection using Conditional Random Fields. In Proc. of COLING-2004, pages 562–568.
    • (2004) Proc. of COLING-2004 , pp. 562-568
    • Peng, Fuchun1    McCallum, Andrew2
  • 9
    • 3042857130 scopus 로고    scopus 로고
    • The Unknown Word Problem: a Morphological Analysis of Japanese Using Maximum Entropy Aided by a Dictionary
    • Kiyotaka Uchimoto, Satoshi Sekine, and Hitoshi Isahara. 2001. The Unknown Word Problem: a Morphological Analysis of Japanese Using Maximum Entropy Aided by a Dictionary. In Proc. of EMNLP-2001, pages 91–99.
    • (2001) Proc. of EMNLP-2001 , pp. 91-99
    • Uchimoto, Kiyotaka1    Sekine, Satoshi2    Isahara, Hitoshi3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.