Large-Scale Semi-Supervised Learning for Natural Language ...

More documents

Recommendations

Info

[Pantel and Lin, 2002] Patrick Pantel and Dekang Lin. Discovering word senses from text.In KDD, 2002.[Pantel and Pennacchiotti, 2006] Patrick Pantel and Marco Pennacchiotti. Espresso: leveraginggeneric patterns for automatically harvesting semantic relations. In ACL ’06: Proceedingsof the 21st International Conference on Computational Linguistics and the 44thannual meeting of the ACL, 2006.[Pantel et al., 2007] Patrick Pantel, Rahul Bhagat, Bonaventura Coppola, TimothyChklovski, and Eduard Hovy. ISP: Learning inferential selectional preferences. InNAACL-HLT, 2007.[Pantel, 2003] Patrick Pantel. Clustering by committee. PhD thesis, University of Alberta,2003.[Paşca et al., 2006] Marius Paşca, Dekang Lin, Jeffrey Bigham, Andrei Lifchits, and AlpaJain. Names and similarities on the Web: Fact extraction in the fast lane. In Proceedingsof the 21st International Conference on Computational Linguistics and 44th AnnualMeeting of the ACL, 2006.[Phan, 2006] Xuan-Hieu Phan. CRFTagger: CRF English POS Tagger. crftagger.sourceforge.net, 2006.[Pinchak and Bergsma, 2007] Christopher Pinchak and Shane Bergsma. Automatic answertyping for how-questions. In HLT-NAACL, 2007.[Pitler et al., 2010] Emily Pitler, Shane Bergsma, Dekang Lin, and Kenneth Church. Usingweb-scale N-grams to improve base NP parsing performance. In COLING, 2010.[Porter, 1980] Martin F. Porter. An algorithm for suffix stripping. Program, 14(3), 1980.[Radev et al., 2001] Dragomir R. Radev, Hong Qi, Zhiping Zheng, Sasha Blair-Goldensohn, Zhu Zhang, Weiguo Fan, and John Prager. Mining the Web for Answers toNatural Language Questions. In CIKM, 2001.[Raina et al., 2006] Rajat Raina, Andrew Y. Ng, and Daphne Koller. Constructing informativepriors using transfer learning. In ICML, 2006.[Rappoport and Levent-Levi, 2006] Ari Rappoport and Tsahi Levent-Levi. Induction ofcross-language affix and letter sequence correspondence. In EACL Workshop on Cross-Language Knowledge Induction, 2006.[Ravichandran and Hovy, 2002] Deepak Ravichandran and Eduard Hovy. Learning surfacetext patterns for a question answering system. In ACL ’02: Proceedings of the 40thAnnual Meeting on Association for Computational Linguistics, 2002.[Resnik, 1996] Philip Resnik. Selectional constraints: An information-theoretic model andits computational realization. Cognition, 61, 1996.[Resnik, 1999] Philip Resnik. Mining the web for bilingual text. In Proceedings of the37th Annual Meeting of the Association for Computational Linguistics, 1999.[Rifkin and Klautau, 2004] Ryan Rifkin and Aldebaro Klautau. In defense of one-vs-allclassification. JMLR, 5:101–141, 2004.[Riloff and Jones, 1999] Ellen Riloff and Rosie Jones. Learning dictionaries for informationextraction by multi-level bootstrapping. In Proceedings of the Sixteenth NationalConference on Artificial Intelligence (AAAI-99), 1999.Adapting a lexicalized-[Rimell and Clark, 2008] Laura Rimell and Stephen Clark.grammar parser to contrasting domains. In EMNLP, 2008.123
[Ristad and Yianilos, 1998] Eric Sven Ristad and Peter N. Yianilos. Learning string-editdistance. IEEE Trans. Pattern Anal. Machine Intell., 20(5), 1998.[Roberto et al., 2007] Basili Roberto, Diego De Cao, Paolo Marocco, and Marco Pennacchiotti.Learning selectional preferences for entailment or paraphrasing rules. In RANLP,2007.[Rooth et al., 1999] Mats Rooth, Stefan Riezler, Detlef Prescher, Glenn Carroll, and FranzBeil. Inducing a semantically annotated lexicon via EM-based clustering. In ACL, 1999.[Roth and Yih, 2004] Dan Roth and Wen-Tau Yih. A linear programming formulation forglobal inference in natural language tasks. In CoNLL, 2004.[Roth, 1998] Dan Roth. Learning to resolve natural language ambiguities: A unified approach.In AAAI/IAAI, 1998.[Russell and Norvig, 2003] Stuart J. Russell and Peter Norvig. Artificial Intelligence: amodern approach, chapter 20: Statistical Learning Methods. Prentice Hall, Upper SaddleRiver, N.J., 2nd edition edition, 2003.[Schafer and Yarowsky, 2002] Charles Schafer and David Yarowsky. Inducing translationlexicons via diverse similarity measures and bridge languages. In CoNLL, 2002.[Sekine, 2008] Satoshi Sekine. A linguistic knowledge discovery tool: Very large ngramdatabase search with arbitrary wildcards. In COLING: Companion volume: Demonstrations,2008.[Shannon, 1948] Claude E. Shannon. A mathematical theory of communication. Bell SystemTechnical Journal, 27(3), 1948.[Shaw and Hatzivassiloglou, 1999] James Shaw and Vasileios Hatzivassiloglou. Orderingamong premodifiers. In ACL, 1999.[Simard et al., 1992] Michel Simard, George F. Foster, and Pierre Isabelle. Using cognatesto align sentences in bilingual corpora. In Fourth International Conference on Theoreticaland Methodological Issues in Machine Translation, 1992.[Smith and Eisner, 2005] Noah A. Smith and Jason Eisner. Contrastive estimation: traininglog-linear models on unlabeled data. In ACL, 2005.[Snow et al., 2005] Rion Snow, Daniel Jurafsky, and Andrew Y. Ng. Learning syntacticpatterns for automatic hypernym discovery. In NIPS, 2005.[Snow et al., 2008] Rion Snow, Brendan O’Connor, Daniel Jurafsky, and Andrew Y. Ng.Cheap and fast - but is it good? evaluating non-expert annotations for natural languagetasks. In EMNLP, 2008.[Steedman, 2008] Mark Steedman. On becoming a discipline. Comput. Linguist.,34(1):137–144, 2008.[Strube et al., 2002] Michael Strube, Stefan Rapp, and Christoph Müller. The influence ofminimum edit distance on reference resolution. In EMNLP, 2002.[Suzuki and Isozaki, 2008] Jun Suzuki and Hideki Isozaki. Semi-supervised sequentiallabeling and segmentation using giga-word scale unlabeled data. In Proceedings of ACL-08: HLT, 2008.[Taskar et al., 2005] Ben Taskar, Simon Lacoste-Julien, and Dan Klein. A discriminativematching approach to word alignment. In HLT-EMNLP, 2005.[Tefas et al., 2001] Anastasios Tefas, Constantine Kotropoulos, and Ioannis Pitas. Usingsupport vector machines to enhance the performance of elastic graph matching for frontalface authentication. IEEE Trans. Pattern Anal. Machine Intell., 23:735–746, 2001.124
Page 1 and 2:
University of AlbertaLarge-Scale Se
Page 5 and 6:
Table of Contents1 Introduction 11.
Page 7 and 8:
7 Alignment-Based Discriminative St
Page 9 and 10:
List of Figures2.1 The linear class
Page 11 and 12:
drawn in by establishing a partial
Page 13 and 14:
(2) “He saw the trophy won yester
Page 15 and 16:
actual sentence said, “My son’s
Page 17 and 18:
Uses Web-Scale N-grams Auto-Creates
Page 19 and 20:
spelling correction, and the identi
Page 21 and 22:
Chapter 2Supervised and Semi-Superv
Page 23 and 24:
emphasis on “deliverables and eva
Page 25 and 26:
Figure 2.1: The linear classifier h
Page 27 and 28:
The above experimental set-up is so
Page 29 and 30:
and discriminative models therefore
Page 31 and 32:
their slack value). In practice, I
Page 33 and 34:
One way to find a better solution i
Page 35 and 36:
Figure 2.2: Learning from labeled a
Page 37 and 38:
algorithm). Yarowsky used it for wo
Page 39 and 40:
Learning with Natural Automatic Exa
Page 41 and 42:
positive examples from any collecti
Page 43 and 44:
generated word clusters. Several re
Page 45 and 46:
One common disambiguation task is t
Page 47 and 48:
3.2.2 Web-Scale Statistics in NLPEx
Page 49 and 50:
For each target wordv 0 , there are
Page 51 and 52:
ut without counts for the class pri
Page 53 and 54:
Accuracy (%)10090807060SUPERLMSUMLM
Page 55 and 56:
We also follow Carlson et al. [2001
Page 57 and 58:
Set BASE [Golding and Roth, 1999] T
Page 59 and 60:
pronoun (#3) guarantees that at the
Page 61 and 62:
807876F-Score747270Stemmed patterns
Page 63 and 64:
anaphoricity by [Denis and Baldridg
Page 65 and 66:
ter, we present a simple technique
Page 67 and 68:
We seek weights such that the class
Page 69 and 70:
each optimum performance is at most
Page 71 and 72:
We now show that ¯w T (diag(¯p)
Page 73 and 74:
Training ExamplesSystem 10 100 1K 1
Page 75 and 76:
Since we wanted the system to learn
Page 77 and 78:
Chapter 5Creating Robust Supervised
Page 79 and 80:
§ In-Domain (IN) Out-of-Domain #1
Page 81 and 82: Adjective ordering is also needed i
Page 83 and 84: Accuracy (%)10095908580757065601001
Page 85 and 86: System IN O1 O2Baseline 66.9 44.6 6
Page 87 and 88: 90% of the time in Gutenberg. The L
Page 89 and 90: VBN/VBD distinction by providing re
Page 91 and 92: other tasks we only had a handful o
Page 93 and 94: without the need for manual annotat
Page 95 and 96: DSP uses these labels to identify o
Page 97 and 98: Semantic classesMotivated by previo
Page 99 and 100: empirical Pr(n|v) in Equation (6.2)
Page 101 and 102: Verb Plaus./Implaus. Resnik Dagan e
Page 103 and 104: SystemAccMost-Recent Noun 17.9%Maxi
Page 105 and 106: Chapter 7Alignment-Based Discrimina
Page 107 and 108: ious measures to learn the recurren
Page 109 and 110: how labeled word pairs can be colle
Page 111 and 112: Figure 7.1: LCSR histogram and poly
Page 113 and 114: 0.711-pt Average Precision0.60.50.4
Page 115 and 116: Fr-En Bitext Es-En Bitext De-En Bit
Page 117 and 118: Chapter 8Conclusions and Future Wor
Page 119 and 120: 8.3 Future WorkThis section outline
Page 121 and 122: My focus is thus on enabling robust
Page 123 and 124: [Bergsma and Cherry, 2010] Shane Be
Page 125 and 126: [Church and Mercer, 1993] Kenneth W
Page 127 and 128: [Grefenstette, 1999] Gregory Grefen
Page 129 and 130: [Koehn, 2005] Philipp Koehn. Europa
Page 131: [Mihalcea and Moldovan, 1999] Rada
Page 135 and 136: [Wang et al., 2008] Qin Iris Wang,
Page 137: NNP noun, proper, singular Motown V
show all

Large-Scale Semi-Supervised Learning for Natural Language ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?