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Volumn , Issue , 2011, Pages 893-901

From News to Comment: Resources and Benchmarks for Parsing the Language of Web 2.0

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL LINGUISTICS; NATURAL LANGUAGE PROCESSING SYSTEMS; SYNTACTICS;

EID: 84876810220     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (65)

References (34)
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  • 3
    • 25844498898 scopus 로고    scopus 로고
    • Intricacies of Collins parsing model
    • Daniel Bikel. 2004. Intricacies of Collins parsing model. Computational Linguistics, 30(4).
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    • Bikel, Daniel1
  • 6
    • 0031366914 scopus 로고    scopus 로고
    • Statistical parsing with a context-free grammar and word statistics
    • Eugene Charniak. 1997. Statistical parsing with a context-free grammar and word statistics. In Proceedings of AAAI.
    • (1997) Proceedings of AAAI
    • Charniak, Eugene1
  • 7
    • 33751194843 scopus 로고    scopus 로고
    • How much can part-of-speech tagging affect parsing?
    • Mary Dalrymple. 2006. How much can part-of-speech tagging affect parsing? Natural Language Engineering, 12(4).
    • (2006) Natural Language Engineering , vol.12 , Issue.4
    • Dalrymple, Mary1
  • 10
    • 80053639120 scopus 로고    scopus 로고
    • cba to check the spelling” investigating parser performance on discussion forum posts
    • Jennifer Foster. 2010. “cba to check the spelling” investigating parser performance on discussion forum posts. In Proceedings of NAACL-HLT.
    • (2010) Proceedings of NAACL-HLT
    • Foster, Jennifer1
  • 12
    • 85035364491 scopus 로고    scopus 로고
    • Svmtool: A general pos tagger generator based on support vector machines
    • Jesús Giménez and Lluís Màrquez. 2004. Svmtool: A general pos tagger generator based on support vector machines. In Proceedings of LREC.
    • (2004) Proceedings of LREC
    • Giménez, Jesús1    Màrquez, Lluís2
  • 14
    • 0038859884 scopus 로고    scopus 로고
    • Exploiting diversity in natural language processing: Combining parsers
    • John C. Henderson and Eric Brill. 2000. Exploiting diversity in natural language processing: Combining parsers. In Proceedings of EMNLP.
    • (2000) Proceedings of EMNLP
    • Henderson, John C.1    Brill, Eric2
  • 15
    • 80053389291 scopus 로고    scopus 로고
    • Self-training PCFG grammars with latent annotations across languages
    • Zhongqiang Huang and Mary Harper. 2009. Self-training PCFG grammars with latent annotations across languages. In Proceedings of EMNLP.
    • (2009) Proceedings of EMNLP
    • Huang, Zhongqiang1    Harper, Mary2
  • 23
    • 80053375807 scopus 로고    scopus 로고
    • Integrating graph-based and transition-based dependency parsers
    • Joakim Nivre and Ryan McDonald. 2008. Integrating graph-based and transition-based dependency parsers. In Proceedings of ACL-HLT.
    • (2008) Proceedings of ACL-HLT
    • Nivre, Joakim1    McDonald, Ryan2
  • 24
    • 84949890107 scopus 로고    scopus 로고
    • Maltparser: A data-driven parser-generator for dependency parsing
    • Joakim Nivre, Johan Hall, and Jens Nilsson. 2006. Maltparser: A data-driven parser-generator for dependency parsing. In Proceedings of LREC.
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    • Nivre, Joakim1    Hall, Johan2    Nilsson, Jens3
  • 29
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    • Products of random latent variable grammars
    • Slav Petrov. 2010. Products of random latent variable grammars. In Proceedings of NAACL-HLT.
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    • Petrov, Slav1
  • 32
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    • Self-training for enhancement and domain adaptation of statistical parsers trained on small datasets
    • Roi Reichart and Ari Rappaport. 2007. Self-training for enhancement and domain adaptation of statistical parsers trained on small datasets. In Proceedings of ACL.
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    • Reichart, Roi1    Rappaport, Ari2
  • 33
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    • Self-training without reranking for parser domain adapation and its impact on semantic role labelling
    • Kenji Sagae. 2010. Self-training without reranking for parser domain adapation and its impact on semantic role labelling. In Proceedings of the ACL Workshop on Domain Adaptation for NLP.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.