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

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sets of cognates across groups of languages. We showed improvements over simple clusteringtechniques that do not inherently consider the transitivity of cognate relations. Wedeveloped our multi-lingual approach via the global inference framework of integer linearprogramming. We followed Roth and Yih [2004] in using binary-{0,1} ILP variables torepresent the decisions made by our system (cognate or not a cognate), and we optimized asour objective function the sum of the costs/scores of the decisions, with constraints for transitivityand one-to-one mappings across languages. Our formulation was partly based onsimilar solutions for other tasks by [Barzilay and Lapata, 2006; Denis and Baldridge, 2007].Application of these techniques should improve the detection of translated sentences as well[Munteanu and Marcu, 2005], since transitivity across languages also applies, of course, atthe sentence level.107
Chapter 8Conclusions and Future Work8.1 SummaryThis dissertation outlined two simple, scalable, effective methods for large-scale semisupervisedlearning: constructing features from web-scale N-gram data, and using unlabeleddata to automatically generate training examples.The availability of web-scale N-gram data was crucial for our improved web-scalefeature-based approaches. While the Google N-gram data was originally created to supportthe language model of an MT system, we confirmed that this data can be useful fora range of tasks, including both analysis and generation problems. Unlike previous workusing search engines, it is possible to extract millions of web-scale counts efficiently fromN-gram data. We can thus freely exploit numerous overlapping and interdependent contextsfor each example, for both training and test instances. Chapter 3 presented a unified frameworkfor integrating such N-gram information for various lexical disambiguation tasks.Excellent results were achieved on three tasks. In particular, we proposed a novel and successfulmethod of using web-scale counts for the identification of non-referential pronouns,a long-standing challenge in the anaphora resolution community.In Chapter 4, we introduced a new form of SVM training to mitigate the dependence ofthe discriminative web-N-gram systems on large amounts of training data. Since the unsupervisedsystem was known to achieve good performance with equal weights, we changedthe SVM’s regularization to prefer low-weight-variance solutions, biasing it toward the unsupervisedsolution. The optimization problem remained a convex function of the featureweights, and was thus theoretically no harder to optimize than a standard SVM. On smalleramounts of training data, the variance-regularization SVM performed dramatically betterthan the standard multi-class SVM.Chapter 5 addressed a pair of open questions on the use of web-scale data in NLP. First,we showed there was indeed a significant benefit in combining web-scale counts with thetraditional features used in state-of-the-art supervised approaches. For example, we proposeda novel system for adjective ordering that exceeds the state-of-the-art performance,without using any N-gram data, and then we further improved the performance of this systemby adding N-gram features. Secondly, and perhaps much more importantly, modelswith web-based features were shown to perform much better than traditional supervisedsystems when moving to new domains or when labeled training data was scarce (realisticsituations for the practical application of NLP technology).In the second part of the dissertation, we showed how to automatically create labeled ex-108
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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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Page 83 and 84: Accuracy (%)10095908580757065601001
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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 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
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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