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

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List of Tables1.1 Summary of tasks handled in the dissertation . . . . . . . . . . . . . . . . 82.1 The classifier confusion matrix . . . . . . . . . . . . . . . . . . . . . . . . 193.1 SUMLM accuracy combining N-grams from order Min to Max . . . . . . . 453.2 Context-sensitive spelling correction accuracy on different confusion sets . 483.3 Pattern filler types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493.4 Human vs. computer non-referential it detection . . . . . . . . . . . . . . . 534.1 Accuracy of preposition-selection SVMs. . . . . . . . . . . . . . . . . . . 644.2 Accuracy of spell-correction SVMs. . . . . . . . . . . . . . . . . . . . . . 644.3 Accuracy of non-referential detection SVMs. . . . . . . . . . . . . . . . . 655.1 Data for tasks in Chapter 5 . . . . . . . . . . . . . . . . . . . . . . . . . . 705.2 Number of labeled examples for tasks in Chapter 5 . . . . . . . . . . . . . 705.3 Adjective ordering accuracy . . . . . . . . . . . . . . . . . . . . . . . . . 735.4 Spelling correction accuracy . . . . . . . . . . . . . . . . . . . . . . . . . 765.5 NC-bracketing accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . 795.6 Verb-POS-disambiguation accuracy . . . . . . . . . . . . . . . . . . . . . 816.1 Pseudodisambiguation results averaged across each example . . . . . . . . 896.2 Selectional ratings for plausible/implausible direct objects . . . . . . . . . 926.3 Recall on identification of Verb-Object pairs from an unseen corpus . . . . 926.4 Pronoun resolution accuracy on nouns in current or previous sentence. . . . 947.1 Foreign-English cognates and false friend training examples. . . . . . . . . 997.2 Bitext French-English development set cognate identification 11-pt averageprecision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1037.3 Bitext, Dictionary Foreign-to-English cognate identification 11-pt average7.4precision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Example features and weights for various Alignment-Based Discriminative103classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1057.5 Highest scored pairs by Alignment-Based Discriminative classifier . . . . . 106
List of Figures2.1 The linear classifier hyperplane . . . . . . . . . . . . . . . . . . . . . . . . 162.2 Learning from labeled and unlabeled examples . . . . . . . . . . . . . . . 263.1 Preposition selection learning curve . . . . . . . . . . . . . . . . . . . . . 443.2 Preposition selection over high-confidence subsets . . . . . . . . . . . . . . 453.33.4Context-sensitive spelling correction learning curve . . . . . . . . . . . . .Non-referential detection learning curve . . . . . . . . . . . . . . . . . . .47513.5 Effect of pattern-word truncation on non-referential it detection. . . . . . . 524.1 Multi-class classification for web-scale N-gram models . . . . . . . . . . . 595.1 In-domain learning curve of adjective ordering classifiers on BNC. . . . . . 745.2 Out-of-domain learning curve of adjective ordering classifiers on Gutenberg. 745.3 Out-of-domain learning curve of adjective ordering classifiers on Medline. . 755.4 In-domain learning curve of spelling correction classifiers on NYT. . . . . . 765.5 Out-of-domain learning curve of spelling correction classifiers on Gutenberg. 775.6 Out-of-domain learning curve of spelling correction classifiers on Medline. 775.7 In-domain NC-bracketer learning curve . . . . . . . . . . . . . . . . . . . 795.8 Out-of-domain learning curve of verb disambiguation classifiers on Medline. 816.1 Disambiguation results by noun frequency. . . . . . . . . . . . . . . . . . . 916.2 Pronoun resolution precision-recall on MUC. . . . . . . . . . . . . . . . . 937.1 LCSR histogram and polynomial trendline of French-English dictionary pairs.1027.2 Bitext French-English cognate identification learning curve. . . . . . . . . 104
Page 1 and 2: University of AlbertaLarge-Scale Se
Page 5 and 6: Table of Contents1 Introduction 11.
Page 7: 7 Alignment-Based Discriminative St
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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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
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how labeled word pairs can be colle
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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
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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
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[Bergsma and Cherry, 2010] Shane Be
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[Church and Mercer, 1993] Kenneth W
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[Grefenstette, 1999] Gregory Grefen
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[Koehn, 2005] Philipp Koehn. Europa
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[Mihalcea and Moldovan, 1999] Rada
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[Ristad and Yianilos, 1998] Eric Sv
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[Wang et al., 2008] Qin Iris Wang,
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NNP noun, proper, singular Motown V
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