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

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like the world wide web. We could also reverse the role of noun and verb in our training,having verb-specific features and discriminating separately for each argument noun. Thelatent information would then be lists of similar nouns.Finally, one potentially very exciting direction for future work would be to automaticallycollect and create features for online images returned for the noun query. It wouldbe amazing to be able to predict noun-verb plausibility purely on the basis of a system’slearned visual recognition of compatible features (e.g., this collection of images seems todepict something that can be eaten, etc.).DSP provides an excellent framework for such explorations because it generates manytraining examples and can therefore incorporate fine-grained, overlapping and potentiallyinterdependent features. We look at another system that has these properties in the followingchapter.95
Chapter 7Alignment-Based DiscriminativeString Similarity“Kimono... kimono... kimono... Ha! Of course! Kimono is come from theGreek word himona, is mean winter. So, what do you wear in the wintertimeto stay warm? A robe. You see: robe, kimono. There you go!”- Gus Portokalos, My Big Fat Greek WeddingThis chapter proposes a new model of string similarity that exploits a character-basedalignment of the two strings. We again adopt a discriminative approach. Positive pairs aregenerated automatically from word pairs with a high association in an aligned bitext, orelse mined from dictionary translations. Negatives are constructed from pairs with a highamount of character overlap, but which are not translations. So in this work, there are threetypes of information that allow us to generate examples automatically: statistics from abitext, entries in a dictionary, and characters in the strings. This information is unlabeledin the sense that no human annotator has specifically labeled cognates in the data. It isuseful because once a definition is adopted, examples can be generated automatically, anddifferent methods can be empirically evaluated on a level playing field.7.1 IntroductionString similarity is often used as a means of quantifying the likelihood that two pairs ofstrings have the same underlying meaning, based purely on the character composition ofthe two words. [Strube et al., 2002] use edit distance [Levenshtein, 1966] as a feature fordetermining if two words are coreferent. [Taskar et al., 2005] use French-English commonletter sequences as a feature for discriminative word alignment in bilingual texts. [Brilland Moore, 2000] learn misspelled-word to correctly-spelled-word similarities for spellingcorrection. In each of these examples, a similarity measure can make use of the recurrentsubstring pairings that reliably occur between words having the same meaning.Across natural languages, these recurrent substring correspondences are found in wordpairs known as cognates: words with a common form and meaning across languages. Cognatesarise either from words in a common ancestor language (e.g. light/Licht, night/Nachtin English/German) or from foreign word borrowings (e.g. trampoline/toranporin in English/Japanese).Knowledge of cognates is useful for a number of applications, including0 A version of this chapter has been published as [Bergsma and Kondrak, 2007a]96
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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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Page 61 and 62: 807876F-Score747270Stemmed patterns
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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
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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
Page 135 and 136: [Wang et al., 2008] Qin Iris Wang,
Page 137: NNP noun, proper, singular Motown V
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