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

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3.7.3 Non-referential Pronoun Detection Data . . . . . . . . . . . . . . . 503.7.4 Non-referential Pronoun Detection Results . . . . . . . . . . . . . 513.7.5 Further Analysis and Discussion . . . . . . . . . . . . . . . . . . . 513.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544 Improved Natural Language Learning via Variance-Regularization SupportVector Machines 554.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564.2 Three Multi-Class SVM Models . . . . . . . . . . . . . . . . . . . . . . . 574.2.14.2.2Standard Multi-Class SVM . . . . . . . . . . . . . . . . . . . . . .SVM with Class-Specific Attributes . . . . . . . . . . . . . . . . .57594.2.3 Variance Regularization SVMs . . . . . . . . . . . . . . . . . . . . 614.3 Experimental Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634.4.1 Preposition Selection . . . . . . . . . . . . . . . . . . . . . . . . . 634.4.2 Context-Sensitive Spelling Correction . . . . . . . . . . . . . . . . 644.4.3 Non-Referential Pronoun Detection . . . . . . . . . . . . . . . . . 654.5 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 664.6 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 664.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675 Creating Robust Supervised Classifiers via Web-Scale N-gram Data 685.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685.2 Experiments and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695.2.1 Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . 695.2.2 Tasks and Labeled Data . . . . . . . . . . . . . . . . . . . . . . . 705.2.3 Web-Scale Auxiliary Data . . . . . . . . . . . . . . . . . . . . . . 715.3 Prenominal Adjective Ordering . . . . . . . . . . . . . . . . . . . . . . . . 715.3.1 Supervised Adjective Ordering . . . . . . . . . . . . . . . . . . . . 725.3.2 Adjective Ordering Results . . . . . . . . . . . . . . . . . . . . . . 735.4 Context-Sensitive Spelling Correction . . . . . . . . . . . . . . . . . . . . 755.4.15.4.2Supervised Spelling Correction . . . . . . . . . . . . . . . . . . .Spelling Correction Results . . . . . . . . . . . . . . . . . . . . .75765.5 Noun Compound Bracketing . . . . . . . . . . . . . . . . . . . . . . . . . 785.5.1 Supervised Noun Bracketing . . . . . . . . . . . . . . . . . . . . . 785.5.2 Noun Compound Bracketing Results . . . . . . . . . . . . . . . . . 785.6 Verb Part-of-Speech Disambiguation . . . . . . . . . . . . . . . . . . . . . 795.6.1 Supervised Verb Disambiguation . . . . . . . . . . . . . . . . . . . 805.6.2 Verb POS Disambiguation Results . . . . . . . . . . . . . . . . . . 815.7 Discussion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 825.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 826 Discriminative Learning of Selectional Preference from Unlabeled Text6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83836.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 846.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 856.3.1 Creating Examples . . . . . . . . . . . . . . . . . . . . . . . . . . 856.3.2 Partitioning for Efficient Training . . . . . . . . . . . . . . . . . . 866.3.3 Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 876.4 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 886.4.1 Set up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 886.4.2 Feature weights . . . . . . . . . . . . . . . . . . . . . . . . . . . . 896.4.3 Pseudodisambiguation . . . . . . . . . . . . . . . . . . . . . . . . 896.4.46.4.5Human Plausibility . . . . . . . . . . . . . . . . . . . . . . . . . .Unseen Verb-Object Identification . . . . . . . . . . . . . . . . . .91926.4.6 Pronoun Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . 936.5 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 94
7 Alignment-Based Discriminative String Similarity 967.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 967.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 977.3 The Cognate Identification Task . . . . . . . . . . . . . . . . . . . . . . . 987.4 Features for Discriminative String Similarity . . . . . . . . . . . . . . . . . 997.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1017.5.1 Bitext Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . 1017.5.2 Dictionary Experiments . . . . . . . . . . . . . . . . . . . . . . . 1027.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1027.7 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 1068 Conclusions and Future Work 1088.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1088.2 The Impact of this Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 1098.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1108.3.1 Improved Learning with Automatically-Generated Examples . . . . 1108.3.2 Exploiting New ML Techniques . . . . . . . . . . . . . . . . . . . 1108.3.3 New NLP Problems . . . . . . . . . . . . . . . . . . . . . . . . . 1108.3.4 Improving Core NLP Technologies . . . . . . . . . . . . . . . . . 1118.3.5 Mining New Data Sources . . . . . . . . . . . . . . . . . . . . . . 112Bibliography 113A Penn Treebank Tag Set 127
Page 1 and 2: University of AlbertaLarge-Scale Se
Page 5: Table of Contents1 Introduction 11.
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
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Set BASE [Golding and Roth, 1999] T
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pronoun (#3) guarantees that at the
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807876F-Score747270Stemmed patterns
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anaphoricity by [Denis and Baldridg
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ter, we present a simple technique
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We seek weights such that the class
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each optimum performance is at most
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We now show that ¯w T (diag(¯p)
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Training ExamplesSystem 10 100 1K 1
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Since we wanted the system to learn
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Chapter 5Creating Robust Supervised
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§ In-Domain (IN) Out-of-Domain #1
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Adjective ordering is also needed i
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Accuracy (%)10095908580757065601001
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System IN O1 O2Baseline 66.9 44.6 6
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90% of the time in Gutenberg. The L
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VBN/VBD distinction by providing re
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other tasks we only had a handful o
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without the need for manual annotat
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DSP uses these labels to identify o
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Semantic classesMotivated by previo
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empirical Pr(n|v) in Equation (6.2)
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Verb Plaus./Implaus. Resnik Dagan e
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SystemAccMost-Recent Noun 17.9%Maxi
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Chapter 7Alignment-Based Discrimina
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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
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0.711-pt Average Precision0.60.50.4
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Fr-En Bitext Es-En Bitext De-En Bit
Page 117 and 118:
Chapter 8Conclusions and Future Wor
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8.3 Future WorkThis section outline
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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
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[Koehn, 2005] Philipp Koehn. Europa
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[Mihalcea and Moldovan, 1999] Rada
Page 133 and 134:
[Ristad and Yianilos, 1998] Eric Sv
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[Wang et al., 2008] Qin Iris Wang,
Page 137:
NNP noun, proper, singular Motown V
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