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

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Accuracy (%)1009590858075706560N-GM+LEXN-GMLEX1001e31e4Number of training examples1e5Figure 5.3: Out-of-domain learning curve of adjective ordering classifiers on Medline.5.4 Context-Sensitive Spelling CorrectionWe now turn to the generation problem of context-sensitive spelling correction. For thosewho have read the previous two chapters, you’re obviously familiar with the task: For everyoccurrence of a word in a pre-defined set of confusable words (like peace and piece), thesystem must select the most likely word from the set, flagging possible usage errors whenthe predicted word disagrees with the original.Our in-domain examples are again from the New York Times (NYT) portion of Gigaword,as described in Chapter 3. Recall that these comprise the 5 confusion sets whereaccuracy was below 90% in [Golding and Roth, 1999]. There are 100K training, 10K development,and 10K test examples for each confusion set. Our results are averages acrossconfusion sets.Out-of-domain examples are again drawn from Gutenberg and Medline. We extract allinstances of words that are in one of our confusion sets, along with surrounding context.By assuming the extracted instances represent correct usage, we label 7.8K and 56K outof-domaintest examples for Gutenberg and Medline, respectively.We test three unsupervised systems: 1) TRIGRAM (Chapter 3): use one token of contexton the left and one on the right, and output the candidate from the confusion set that occursmost frequently in this pattern [Lapata and Keller, 2005]. 2) SUMLM (Chapter 3), wherewe measure the frequency of the candidates in all the 3-to-5-gram patterns that span theconfusable word. For each candidate, we sum the log-counts of all patterns filled with thecandidate, and output the candidate with the highest total. 3) The baseline predicts the mostfrequent member of each confusion set, based on frequencies in the NYT training data.5.4.1 Supervised Spelling CorrectionOur LEX features are typical disambiguation features that flag specific aspects of the context.We have features for the words at all positions in a 9-word window (called collocationfeatures by [Golding and Roth, 1999]), plus indicators for a particular word preceding orfollowing the confusable word. We also include indicators for all N-grams, and their position,in a 9-word window.For N-GM count features, we follow Chapter 3. We include the log-counts of all75
System IN O1 O2Baseline 66.9 44.6 60.6TRIGRAM 88.4 78.0 87.4SUMLM 94.8 87.7 94.2SVM with N-GM features 95.7 92.1 93.9SVM with LEX features 95.2 85.8 91.0SVM with N-GM + LEX 96.5 91.9 94.8Table 5.4: Spelling correction accuracy (%). SVM trained on NYT, tested on NYT (IN)and out-of-domain Gutenberg (O1) and Medline (O2).10095Accuracy (%)90858075701001e3N-GM+LEXN-GMLEX1e4Number of training examples1e5Figure 5.4: In-domain learning curve of spelling correction classifiers on NYT.N-grams that span the confusable word, with each word in the confusion set filling theN-gram pattern. These features do not use part-of-speech. Following our previous work,we get N-gram counts using the original Google N-gram Corpus.While neither our LEX nor N-GM features are novel on their own, they have, perhapssurprisingly, not yet been evaluated in a single model.5.4.2 Spelling Correction ResultsThe N-GM features outperform the LEX features, 95.7% vs. 95.2% (Table 5.4). Together,they achieve a very strong 96.5% in-domain accuracy. This is 2% higher than the bestunsupervised approach, SUMLM. Web-based models again perform well across a range oftraining data sizes (Figure 5.4).The error rate of LEX nearly triples on Gutenberg and almost doubles on Medline (Table5.4). Removing N-GM features from the N-GM + LEX system, errors increase around75% on both Gutenberg and Medline. The LEX features provide no help to the combinedsystem on Gutenberg, while they do help significantly on Medline. Note the learningcurves for N-GM+LEX on Gutenberg and Medline (Figures 5.5, and 5.6) do not display thedecrease that we observed in adjective ordering (Figure 5.2).Both the baseline and LEX perform poorly on Gutenberg. The baseline predicts themajority class from NYT, but it’s not always the majority class in Gutenberg. For example,while in NYT site occurs 87% of the time for the (cite, sight, site) confusion set, sight occurs76
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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
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
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
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Page 71 and 72: We now show that ¯w T (diag(¯p)
Page 73 and 74: Training ExamplesSystem 10 100 1K 1
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Page 77 and 78: Chapter 5Creating Robust Supervised
Page 79 and 80: § In-Domain (IN) Out-of-Domain #1
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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)
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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
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
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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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