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

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• since many more training examples are available when examples are created automatically,we can exploit richer, more powerful, more fine-grained features for a rangeof problems, from semantics (Chapter 6) to string similarity (Chapter 7).The new features we proposed include the first use of character-level (string and capitalization)features for selectional preferences (Chapter 6), and the first use of alignment indiscriminative string similarity (Chapter 7).I really do hope you enjoy finding out more about these and the other contributions ofthis dissertation!11
Chapter 2Supervised and Semi-SupervisedMachine Learning in NaturalLanguage Processing“We shape our tools. And then our tools shape us.”- Marshall McLuhanThis chapter outlines the key concepts from machine learning that are used in this dissertation.Section 2.1 provides some musings on why machine learning has risen to besuch a dominant force in NLP. Section 2.2 introduces the linear classifier, the fundamentalstatistical model that we use in all later chapters of the dissertation. Section 2.2, andthe following Section 2.3, address one important goal of this chapter: to present a simple,detailed explanation of how the tools of supervised machine learning can be used inNLP. Sections 2.4 and 2.5 provide a higher-level discussion of related approaches to unsupervisedand semi-supervised learning. In particular, these sections relate past trends insemi-supervised learning to the models presented in the dissertation.2.1 The Rise of Machine Learning in NLPIt is interesting to trace the historical development of the statistical techniques that areso ubiquitous in NLP today. The following mostly relies on the brief historical sketch inChapter 1 of Jurafsky and Martin’s textbook [Jurafsky and Martin, 2000], with insightsfrom [Church and Mercer, 1993; Manning and Schütze, 1999; Jelinek, 2005; Fung andRoth, 2005; Hajič and Hajičová, 2007].The foundations of speech and language processing lie in the 1940s and 1950s, whenfinite-state machines were applied to natural language by Claude Shannon [1948], and subsequentlyanalyzed as a formal language by Noam Chomsky [1956]. During the later 1950sand 1960s, speech and language processing had split into two distinct lines of research:logic-based “symbolic” methods and probabilistic “stochastic” research.Researchers in the symbolic tradition were both pursuing computational approaches toformal language theory and syntax, and also working with natural language in the logicand reasoning framework then being developed in the new field of artificial intelligence.From about 1960 to 1985, stochastic approaches were generally out-of-favour, and remain12
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: spelling correction, and the identi
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
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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
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Page 53 and 54: Accuracy (%)10090807060SUPERLMSUMLM
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Page 59 and 60: pronoun (#3) guarantees that at the
Page 61 and 62: 807876F-Score747270Stemmed patterns
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Page 67 and 68: We seek weights such that the class
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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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