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

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10095Accuracy (%)90858075701001e3N-GM+LEXN-GMLEX1e41e5Number of training examplesFigure 5.5: Out-of-domain learning curve of spelling correction classifiers on Gutenberg.10095Accuracy (%)90858075701001e3N-GM+LEXN-GMLEX1e41e5Number of training examplesFigure 5.6: Out-of-domain learning curve of spelling correction classifiers on Medline.77
90% of the time in Gutenberg. The LEX classifier exploits this bias as it is regularizedtoward a more economical model, but the bias does not transfer to the new domain.5.5 Noun Compound BracketingAbout 70% of web queries are noun phrases [Barr et al., 2008] and methods that can reliablyparse these phrases are of great interest in NLP. For example, a web query for zebra hairstraightener should be bracketed as (zebra (hair straightener)), a stylish hair straightenerwith zebra print, rather than ((zebra hair) straightener), a useless product since the fur ofzebras is already quite straight.The noun compound (NC) bracketing task is usually cast as a decision whether a 3-word NC has a left or right bracketing. Most approaches are unsupervised, using a largecorpus to compare the statistical association between word pairs in the NC. The adjacencymodel [Marcus, 1980] proposes a left bracketing if the association between words one andtwo is higher than between two and three. The dependency model [Lauer, 1995a] comparesone-two vs. one-three. We include dependency model results using PMI as the associationmeasure; results were lower with the adjacency model.As in-domain data, we use [Vadas and Curran, 2007a]’s Wall-Street Journal (WSJ)data, an extension of the Treebank (which originally left NPs flat). We extract all sequencesof three consecutive common nouns, generating 1983 examples from sections 0-22 of theTreebank as training, 72 from section 24 for development and 95 from section 23 as a testset. As out-of-domain data, we use 244 NCs from Grolier Encyclopedia [Lauer, 1995a] and429 NCs from Medline [Nakov, 2007].The majority class baseline is left-bracketing.5.5.1 Supervised Noun BracketingOur LEX features indicate the specific noun at each position in the compound, plus the threepairs of nouns and the full noun triple. We also add features for the capitalization pattern ofthe sequence.N-GM features give the log-count of all subsets of nouns in the compound: (N1), (N2),(N3), (N1 N2), (N1 N3), (N2 N3), and (N1 N2 N3). Counts are from Google V2. Following[Nakov and Hearst, 2005a], we also include counts of noun pairs collapsed into a singletoken; if a pair occurs often on the web as a single unit, it strongly indicates the pair is aconstituent.[Vadas and Curran, 2007a] use simpler features, e.g. they do not use collapsed paircounts. They achieve 89.9% in-domain on WSJ and 80.7% on Grolier. [Vadas and Curran,2007b] use comparable features to ours, but do not test out-of-domain.5.5.2 Noun Compound Bracketing ResultsN-GM systems perform much better on this task (Table 5.5). N-GM+LEX is statistically significantlybetter than LEX on all sets. In-domain, errors more than double without N-GMfeatures. LEX performs poorly here because there are far fewer training examples. Thelearning curve (Figure 5.7) looks much like earlier in-domain curves (Figures 5.1 and 5.4),but truncated before LEX becomes competitive. The absence of a sufficient amount of labeleddata explains why NC-bracketing is generally regarded as a task where corpus countsare crucial.78
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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
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
Page 63 and 64: anaphoricity by [Denis and Baldridg
Page 65 and 66: ter, we present a simple technique
Page 67 and 68: We seek weights such that the class
Page 69 and 70: 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: System IN O1 O2Baseline 66.9 44.6 6
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 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,
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NNP noun, proper, singular Motown V
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