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

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University of AlbertaLarge-Scale Se
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Table of Contents1 Introduction 11.
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7 Alignment-Based Discriminative St
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List of Figures2.1 The linear class
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drawn in by establishing a partial
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(2) “He saw the trophy won yester
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actual sentence said, “My son’s
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Uses Web-Scale N-grams Auto-Creates
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spelling correction, and the identi
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Chapter 2Supervised and Semi-Superv
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emphasis on “deliverables and eva
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Figure 2.1: The linear classifier h
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The above experimental set-up is so
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and discriminative models therefore
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their slack value). In practice, I
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One way to find a better solution i
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Figure 2.2: Learning from labeled a
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algorithm). Yarowsky used it for wo
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Learning with Natural Automatic Exa
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positive examples from any collecti
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generated word clusters. Several re
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One common disambiguation task is t
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3.2.2 Web-Scale Statistics in NLPEx
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For each target wordv 0 , there are
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ut without counts for the class pri
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Accuracy (%)10090807060SUPERLMSUMLM
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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
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: My focus is thus on enabling robust
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 and 132: [Mihalcea and Moldovan, 1999] Rada
Page 133 and 134: [Ristad and Yianilos, 1998] Eric Sv
Page 135 and 136: [Wang et al., 2008] Qin Iris Wang,
Page 137: NNP noun, proper, singular Motown V
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