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Volumn 40, Issue 1, 2014, Pages 85-120

Learning Representations for Weakly Supervised Natural Language Processing Tasks

Author keywords

[No Author keywords available]

Indexed keywords

HIDDEN MARKOV MODELS; IMAGE SEGMENTATION; LABELED DATA; NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84897743792     PISSN: 08912017     EISSN: 15309312     Source Type: Journal    
DOI: 10.1162/COLI_a_00167     Document Type: Article
Times cited : (39)

References (99)
  • 2
    • 84859921107 scopus 로고    scopus 로고
    • A high-performance semi-supervised learning method for text chunking
    • In Ann Arbor, MI
    • Ando, Rie Kubota and Tong Zhang. 2005. A high-performance semi-supervised learning method for text chunking. In Proceedings of the ACL, pages 1-9, Ann Arbor, MI.
    • (2005) Proceedings of the ACL , pp. 1-9
    • Ando, R.K.1    Zhang, T.2
  • 4
    • 85119091952 scopus 로고    scopus 로고
    • Part of speech tagging in context
    • In Geneva
    • Banko, Michele and Robert C. Moore. 2004. Part of speech tagging in context. In Proceedings of the COLING, pages 556-561, Geneva.
    • (2004) Proceedings of the COLING , pp. 556-561
    • Banko, M.1    Moore, R.C.2
  • 7
    • 79959407847 scopus 로고    scopus 로고
    • Neural net language models
    • Bengio, Yoshua. 2008. Neural net language models. Scholarpedia, 3(1):3,881.
    • (2008) Scholarpedia , vol.3 , Issue.1 , pp. 3881
    • Bengio, Y.1
  • 11
    • 25844498898 scopus 로고    scopus 로고
    • Intricacies of Collins' parsing model
    • Bikel, Daniel M. 2004b. Intricacies of Collins' parsing model. Computational Linguistics, 30(4):479-511.
    • (2004) Computational Linguistics , vol.30 , Issue.4 , pp. 479-511
    • Bikel, D.M.1
  • 15
    • 48449092946 scopus 로고    scopus 로고
    • Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
    • In Prague
    • Blitzer, John, Mark Dredze, and Fernando Pereira. 2007. Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Association for Computational Linguistics (ACL), pages 40-47, Prague.
    • (2007) Association for Computational Linguistics (ACL) , pp. 40-47
    • Blitzer, J.1    Dredze, M.2    Pereira, F.3
  • 16
    • 80053342456 scopus 로고    scopus 로고
    • Domain adaptation with structural correspondence learning
    • In Sydney
    • Blitzer, John, Ryan McDonald, and Fernando Pereira. 2006. Domain adaptation with structural correspondence learning. In Proceedings of the EMNLP, pages 120-128, Sydney.
    • (2006) Proceedings of the EMNLP , pp. 120-128
    • Blitzer, J.1    McDonald, R.2    Pereira, F.3
  • 20
    • 84866875199 scopus 로고    scopus 로고
    • Improving generative statistical parsing with semi-supervised word clustering
    • In Paris
    • Candito, Marie and Benoit Crabbe. 2009. Improving generative statistical parsing with semi-supervised word clustering. In Proceedings of the IWPT, pages 138-141, Paris.
    • (2009) Proceedings of the IWPT , pp. 138-141
    • Candito, M.1    Crabbe, B.2
  • 22
    • 84890506043 scopus 로고    scopus 로고
    • Adaptation of maximum entropy classifier: Little data can help a lot
    • In Barcelona
    • Chelba, Ciprian and Alex Acero. 2004. Adaptation of maximum entropy classifier: Little data can help a lot. In Proceedings of the EMNLP, pages 285-292, Barcelona.
    • (2004) Proceedings of the EMNLP , pp. 285-292
    • Chelba, C.1    Acero, A.2
  • 23
    • 56449095373 scopus 로고    scopus 로고
    • A unified architecture for natural language processing: Deep neural networks with multitask learning
    • In Helsinki
    • Collobert, Robert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the International Conference on Machine Learning (ICML), pages 160-167, Helsinki.
    • (2008) Proceedings of the International Conference on Machine Learning (ICML) , pp. 160-167
    • Collobert, R.1    Weston, J.2
  • 25
    • 0001637134 scopus 로고
    • Markov fields and log-linear interaction models for contingency tables
    • Darroch, J. N., S. L. Lauritzen, and T. P. Speed. 1980. Markov fields and log-linear interaction models for contingency tables. The Annals of Statistics, 8(3):522-539.
    • (1980) The Annals of Statistics , vol.8 , Issue.3 , pp. 522-539
    • Darroch, J.N.1    Lauritzen, S.L.2    Speed, T.P.3
  • 26
    • 84860513476 scopus 로고    scopus 로고
    • Frustratingly easy domain adaptation
    • In Prague
    • Daumé III, Hal. 2007. Frustratingly easy domain adaptation. In Proceedings of the ACL, pages 256-263, Prague.
    • (2007) Proceedings of the ACL , pp. 256-263
    • Daumé III, H.1
  • 34
    • 84860503886 scopus 로고    scopus 로고
    • Sparse information extraction: Unsupervised language models to the rescue
    • In Prague
    • Downey, Doug, Stefan Schoenmackers, and Oren Etzioni. 2007. Sparse information extraction: Unsupervised language models to the rescue. In Proceedings of the ACL, pages 696-703, Prague.
    • (2007) Proceedings of the ACL , pp. 696-703
    • Downey, D.1    Schoenmackers, S.2    Etzioni, O.3
  • 35
    • 79951490162 scopus 로고    scopus 로고
    • Online methods for multi-domain learning and adaptation
    • In Honolulu, HI
    • Dredze, Mark and Koby Crammer. 2008. Online methods for multi-domain learning and adaptation. In Proceedings of EMNLP, pages 689-697, Honolulu, HI.
    • (2008) Proceedings of EMNLP , pp. 689-697
    • Dredze, M.1    Crammer, K.2
  • 36
    • 78650226247 scopus 로고    scopus 로고
    • Multi-domain learning by confidence weighted parameter combination
    • Dredze, Mark, Alex Kulesza, and Koby Crammer. 2010. Multi-domain learning by confidence weighted parameter combination. Machine Learning, 79:123-149.
    • (2010) Machine Learning , vol.79 , pp. 123-149
    • Dredze, M.1    Kulesza, A.2    Crammer, K.3
  • 37
    • 79959818548 scopus 로고    scopus 로고
    • Hierarchical Bayesian domain adaptation
    • In Boulder, CO
    • Finkel, Jenny Rose and Christopher D. Manning. 2009. Hierarchical Bayesian domain adaptation. In Proceedings of HLT-NAACL, pages 602-610, Boulder, CO.
    • (2009) Proceedings of HLT-NAACL , pp. 602-610
    • Finkel, J.R.1    Manning, C.D.2
  • 39
    • 0031268341 scopus 로고    scopus 로고
    • Factorial hidden Markov models
    • Ghahramani, Zoubin and Michael I. Jordan. 1997. Factorial hidden Markov models. Machine Learning, 29(2-3):245-273.
    • (1997) Machine Learning , vol.29 , Issue.2-3 , pp. 245-273
    • Ghahramani, Z.1    Jordan, M.I.2
  • 41
    • 84860525845 scopus 로고    scopus 로고
    • A fully Bayesian approach to unsupervised part-of-speech tagging
    • In Prague
    • Goldwater, Sharon and Thomas L. Griffiths. 2007. A fully Bayesian approach to unsupervised part-of-speech tagging. In Proceedings of the ACL, pages 744-751, Prague.
    • (2007) Proceedings of the ACL , pp. 744-751
    • Goldwater, S.1    Griffiths, T.L.2
  • 43
    • 0000679216 scopus 로고
    • Distributional structure
    • Harris, Z. 1954. Distributional structure. Word, 10(23):146-162.
    • (1954) Word , vol.10 , Issue.23 , pp. 146-162
    • Harris, Z.1
  • 44
    • 85040151721 scopus 로고
    • Noun classification from predicage-argument structures
    • In Pittsburgh, PA
    • Hindle, Donald. 1990. Noun classification from predicage-argument structures. In Proceedings of the ACL, pages 268-275, Pittsburgh, PA.
    • (1990) Proceedings of the ACL , pp. 268-275
    • Hindle, D.1
  • 45
    • 0345331815 scopus 로고    scopus 로고
    • Self-organizing maps of words for natural language processing applications
    • In Millet, Alberta
    • Honkela, Timo. 1997. Self-organizing maps of words for natural language processing applications. In Proceedings of the International ICSC Symposium on Soft Computing, pages 401-407, Millet, Alberta.
    • (1997) Proceedings of the International ICSC Symposium on Soft Computing , pp. 401-407
    • Honkela, T.1
  • 49
    • 84860538689 scopus 로고    scopus 로고
    • Instance weighting for domain adaptation in NLP
    • In Prague
    • Jiang, Jing and ChengXiang Zhai. 2007a. Instance weighting for domain adaptation in NLP. In Proceedings of ACL, pages 264-271, Prague.
    • (2007) Proceedings of ACL , pp. 264-271
    • Jiang, J.1    Zhai, C.2
  • 51
    • 80053381171 scopus 로고    scopus 로고
    • Why doesn't EM find good HMM POS-taggers
    • In Prague
    • Johnson, Mark. 2007. Why doesn't EM find good HMM POS-taggers. In Proceedings of the EMNLP, pages 296-305, Prague.
    • (2007) Proceedings of the EMNLP , pp. 296-305
    • Johnson, M.1
  • 52
    • 0031625017 scopus 로고    scopus 로고
    • Dimensionality reduction by random mapping: Fast similarity computation for clustering
    • In Washington, DC
    • Kaski, S. 1998. Dimensionality reduction by random mapping: Fast similarity computation for clustering. In Proceedings of the IJCNN, pages 413-418, Washington, DC.
    • (1998) Proceedings of the IJCNN , pp. 413-418
    • Kaski, S.1
  • 54
    • 85185398851 scopus 로고    scopus 로고
    • Phrase clustering for discriminative learning
    • In Singapore
    • Lin, Dekang and XiaoyunWu. 2009. Phrase clustering for discriminative learning. In Proceedings of the ACL-IJCNLP, pages 1,030-1,038, Singapore.
    • (2009) Proceedings of the ACL-IJCNLP , pp. 1030-1038
    • Lin, D.1    Wu, X.2
  • 55
    • 33646887390 scopus 로고
    • On the limited memory method for large scale optimization
    • Liu, Dong C. and Jorge Nocedal. 1989. On the limited memory method for large scale optimization. Mathematical Programming B, 45(3):503-528.
    • (1989) Mathematical Programming B , vol.45 , Issue.3 , pp. 503-528
    • Liu, D.C.1    Nocedal, J.2
  • 57
    • 34249852033 scopus 로고
    • Building a large annotated corpus of English: The Penn Treebank
    • Marcus, Mitchell P., Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2):313-330.
    • (1993) Computational Linguistics , vol.19 , Issue.2 , pp. 313-330
    • Marcus, M.P.1    Marcinkiewicz, M.A.2    Santorini, B.3
  • 58
    • 0032049073 scopus 로고    scopus 로고
    • Algorithms for bigram and trigram word clustering
    • Martin, Sven, Jorg Liermann, and Hermann Ney. 1998. Algorithms for bigram and trigram word clustering. Speech Communication, 24:19-37.
    • (1998) Speech Communication , vol.24 , pp. 19-37
    • Martin, S.1    Liermann, J.2    Ney, H.3
  • 64
    • 61849109290 scopus 로고    scopus 로고
    • Improving a statistical language model through non-linear prediction
    • Mnih, Andriy, Zhang Yuecheng, and Geoffrey Hinton. 2009. Improving a statistical language model through non-linear prediction. Neurocomputing, 72(7-9):1414-1418.
    • (2009) Neurocomputing , vol.72 , Issue.7-9 , pp. 1414-1418
    • Mnih, A.1    Yuecheng, Z.2    Hinton, G.3
  • 69
    • 84863352175 scopus 로고    scopus 로고
    • Towards robust semantic role labeling
    • In Rochester, NY
    • Pradhan, Sameer, WayneWard, and James H. Martin. 2007. Towards robust semantic role labeling. In Proceedings of NAACL-HLT, pages 556-563, Rochester, NY.
    • (2007) Proceedings of NAACL-HLT , pp. 556-563
    • Pradhan, S.1    Ward, W.2    Martin, J.H.3
  • 70
    • 0024610919 scopus 로고
    • A tutorial on hidden Markov models and selected applications in speech recognition
    • Rabiner, Lawrence R. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-285.
    • (1989) Proceedings of the IEEE , vol.77 , Issue.2 , pp. 257-285
    • Rabiner, L.R.1
  • 73
    • 34249971816 scopus 로고
    • Self-organizing semantic maps
    • Ritter, H. and T. Kohonen. 1989. Self-organizing semantic maps. Biological Cybernetics, 61(4):241-254.
    • (1989) Biological Cybernetics , vol.61 , Issue.4 , pp. 241-254
    • Ritter, H.1    Kohonen, T.2
  • 79
    • 38049120269 scopus 로고    scopus 로고
    • Domain adaptation of conditional probability models via feature subsetting
    • In Warsaw
    • Satpal, Sandeep and Sunita Sarawagi. 2007. Domain adaptation of conditional probability models via feature subsetting. In Proceedings of ECML/PKDD, pages 224-235, Warsaw.
    • (2007) Proceedings of ECML/PKDD , pp. 224-235
    • Satpal, S.1    Sarawagi, S.2
  • 81
    • 84860520429 scopus 로고    scopus 로고
    • Guided learning for bidirectional sequence classification
    • In Prague
    • Shen, Libin, Giorgio Satta, and Aravind K. Joshi. 2007. Guided learning for bidirectional sequence classification. In Proceedings of the ACL, pages 760-767, Prague.
    • (2007) Proceedings of the ACL , pp. 760-767
    • Shen, L.1    Satta, G.2    Joshi, A.K.3
  • 83
    • 33947615175 scopus 로고    scopus 로고
    • Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data
    • Sutton, Charles, Andrew McCallum, and Khashayar Rohanimanesh. 2007. Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data. Journal of Machine Learning Research, 8:693-723.
    • (2007) Journal of Machine Learning Research , vol.8 , pp. 693-723
    • Sutton, C.1    McCallum, A.2    Rohanimanesh, K.3
  • 85
    • 80053399428 scopus 로고    scopus 로고
    • An empirical study of semi-supervised structured conditional models for dependency parsing
    • In Singapore
    • Suzuki, Jun, Hideki Isozaki, Xavier Carreras, and Michael Collins. 2009. An empirical study of semi-supervised structured conditional models for dependency parsing. In Proceedings of the EMNLP, pages 551-560, Singapore.
    • (2009) Proceedings of the EMNLP , pp. 551-560
    • Suzuki, J.1    Isozaki, H.2    Carreras, X.3    Collins, M.4
  • 88
    • 79551484033 scopus 로고    scopus 로고
    • A Bayesian LDA-based model for semi-supervised part-of-speech tagging
    • In Vancouver
    • Toutanova, Kristina and Mark Johnson. 2007. A Bayesian LDA-based model for semi-supervised part-of-speech tagging. In Proceedings of the NIPS, pages 1,521-1,528, Vancouver.
    • (2007) Proceedings of the NIPS , pp. 1521-1528
    • Toutanova, K.1    Johnson, M.2
  • 89
    • 85059937681 scopus 로고    scopus 로고
    • Morphological features help POS tagging of unknown words across language varieties
    • In Jeju Island
    • Tseng, Huihsin, Daniel Jurafsky, and Christopher Manning. 2005. Morphological features help POS tagging of unknown words across language varieties. In Proceedings of the Fourth SIGHAN Workshop, pages 32-39, Jeju Island.
    • (2005) Proceedings of the Fourth SIGHAN Workshop , pp. 32-39
    • Tseng, H.1    Jurafsky, D.2    Manning, C.3
  • 92
    • 77952700189 scopus 로고    scopus 로고
    • From frequency to meaning: Vector space models of semantics
    • Turney, Peter D. and Patrick Pantel. 2010. From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research, 37:141-188.
    • (2010) Journal of Artificial Intelligence Research , vol.37 , pp. 141-188
    • Turney, P.D.1    Pantel, P.2
  • 98
    • 84888160097 scopus 로고    scopus 로고
    • Overcoming the memory bottleneck in distributed training of latent variable models of text
    • In Atlanta, GA
    • Yang, Yi, Alexander Yates, and Doug Downey. 2013. Overcoming the memory bottleneck in distributed training of latent variable models of text. In Proceedings of the NAACL-HLT, pages 579-584, Atlanta, GA.
    • (2013) Proceedings of the NAACL-HLT , pp. 579-584
    • Yang, Y.1    Yates, A.2    Downey, D.3
  • 99
    • 84859984064 scopus 로고    scopus 로고
    • Multilingual dependency learning: A huge feature engineering method to semantic dependency parsing
    • In Boulder, CO
    • Zhao, Hai, Wenliang Chen, Chunyu Kit, and Guodong Zhou. 2009. Multilingual dependency learning: A huge feature engineering method to semantic dependency parsing. In Proceedings of the CoNLL 2009 Shared Task, pages 55-60, Boulder, CO.
    • (2009) Proceedings of the CoNLL 2009 Shared Task , pp. 55-60
    • Zhao, H.1    Chen, W.2    Kit, C.3    Zhou, G.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.