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

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1. Is there a benefit in combining web-scale counts with the standard features used instate-of-the-art supervised approaches?2. How well do web-based models perform on new domains or when labeled data isscarce?We address these questions on two generation and two analysis tasks, using both existingN-gram data and a novel web-scale N-gram corpus that includes part-of-speech information(Section 5.2). While previous work has combined web-scale features with otherfeatures in specific classification problems [Modjeska et al., 2003; Yang et al., 2005; Vadasand Curran, 2007b; Tratz and Hovy, 2010], we provide a multi-task, multi-domain comparison.Some may question why supervised learning with standard features is needed at all forgeneration problems. Why not solely rely on direct evidence from a giant corpus? Forexample, for the task of prenominal adjective ordering (Section 5.3), a system that needs todescribe a ball that is both big and red can simply check that big red is more common onthe web than red big, and order the adjectives accordingly.It is, however, suboptimal to only use simple counts from N-gram data. For example,ordering adjectives by direct web evidence performs 7% worse than our best supervisedsystem (Section 5.3.2). No matter how large the web becomes, there will always be plausibleconstructions that never occur. For example, there are currently no pages indexedby Google with the preferred adjective ordering for bedraggled 56-year-old [professor].Also, in a particular domain, words may have a non-standard usage. Systems trained onlabeled data can learn the domain usage and leverage other regularities, such as suffixes andtransitivity for adjective ordering.With these benefits, systems trained on labeled data have become the dominant technologyin academic NLP. There is a growing recognition, however, that these systems arehighly domain dependent. For example, parsers trained on annotated newspaper text performpoorly on other genres [Gildea, 2001]. While many approaches have adapted NLPsystems to specific domains [Tsuruoka et al., 2005; McClosky et al., 2006b; Blitzer et al.,2007; Daumé III, 2007; Rimell and Clark, 2008], these techniques assume the system knowson which domain it is being used, and that it has access to representative data in that domain.These assumptions are unrealistic in many real-world situations; for example, whenautomatically processing a heterogeneous collection of web pages. How well do supervisedand unsupervised NLP systems perform when used uncustomized, out-of-the-box on newdomains, and how can we best design our systems for robust open-domain performance?Our results show that using web-scale N-gram data in supervised systems advancesthe state-of-the-art performance on standard analysis and generation tasks. More importantly,when operating out-of-domain, or when labeled data is not plentiful, using web-scaleN-gram data not only helps achieve good performance – it is essential.5.2 Experiments and Data5.2.1 Experimental DesignWe again evaluate the benefit of N-gram data on multi-class classification problems. Foreach task, we have some labeled data indicating the correct output for each example. Weevaluate with accuracy: the percentage of examples correctly classified in test data. We69
§ In-Domain (IN) Out-of-Domain #1 (O1) Out-of-Domain #2 (O2)5.3 BNC [Malouf, 2000] Gutenberg (new) Medline (new)5.4 NYT [Bergsma et al., 2009b] Gutenberg (new) Medline (new)5.5 WSJ [Vadas and Curran, 2007a] Grolier [Lauer, 1995a] Medline [Nakov, 2007]5.6 WSJ [Marcus et al., 1993] Brown [Marcus et al., 1993] Medline [Kulick et al., 2004]Table 5.1: Data, with references, for tasks in § 5.3: Prenominal Adjective Ordering, § 5.4:Context-Sensitive Spelling Correction, § 5.5: Noun Compound Bracketing, and§5.6: VerbPart-of-Speech Disambiguation.§ IN-Train IN-Dev IN-Test O1 O25.3 237K 13K 13K 13K 9.1K5.4 100K 50K 50K 7.8K 56K5.5 2.0K 72 95 244 4295.6 23K 1.1K 1.1K 21K 6.3KTable 5.2: Number of labeled examples in in-domain training, development and test sets,and out-of-domain test sets, for tasks in Sections 5.3-5.6.use one in-domain and two out-of-domain test sets for each task. Statistical significance isassessed with McNemar’s test, p
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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
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
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Page 83 and 84: Accuracy (%)10095908580757065601001
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Page 97 and 98: Semantic classesMotivated by previo
Page 99 and 100: empirical Pr(n|v) in Equation (6.2)
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Page 103 and 104: SystemAccMost-Recent Noun 17.9%Maxi
Page 105 and 106: Chapter 7Alignment-Based Discrimina
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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
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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
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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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