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

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Chapter 3Learning with Web-Scale N-gramModelsXKCD comic: Dangers, http://xkcd.com/369/3.1 IntroductionMany problems in Natural Language Processing (NLP) can be viewed as assigning labels toparticular words in text, given the word’s context. If the decision process requires choosinga label from a predefined set of possible choices, called a candidate set or confusion set, theprocess is often referred to as disambiguation [Roth, 1998]. Part-of-speech tagging, spellingcorrection, and word sense disambiguation are all lexical disambiguation processes.0 A version of this chapter has been published as [Bergsma et al., 2008b; 2009b]35
One common disambiguation task is the identification of word-choice errors in text. Alanguage checker can flag an error if a confusable alternative better fits a given context:(1) The system tried to decide {among, between} the two confusable words.Most NLP systems resolve such ambiguity with the help of a large corpus of text. Thecorpus indicates which candidate is more frequent in similar contexts. The larger the corpus,the more accurate the disambiguation [Banko and Brill, 2001]. Since few corpora are aslarge as the world wide web, 1 many systems incorporate web counts into their selectionprocess.For the above example, a typical web-based system would query a search engine withthe sequences “decide among the” and “decide between the” and select the candidate thatreturns the most pages [Lapata and Keller, 2005]. Clearly, this approach fails when morecontext is needed for disambiguation.We propose a unified view of using web-scale data for lexical disambiguation. Ratherthan using a single context sequence, we use contexts of various lengths and positions.There are five 5-grams, four 4-grams, three trigrams and two bigrams spanning the targetword in Example (1). We gather counts for each of these sequences, with each candidatein the target position. We first show how the counts can be used as features in a supervisedclassifier, with a count’s contribution weighted by its context’s size and position. We alsopropose a novel unsupervised system that simply sums a subset of the (log) counts for eachcandidate. Surprisingly, this system achieves most of the gains of the supervised approachwithout requiring any training data.Since we make use of features derived from the distribution of patterns in large amountsof unlabeled data, this work is an instance of a semi-supervised approach in the category,“Using Features from Unlabeled Data,” discussed in Chapter 2, Section 2.5.5.In Section 3.2, we discuss the range of problems that fit the lexical disambiguationframework, and also discuss previous work using the web as a corpus. In Section 3.3 wediscuss our general disambiguation methodology. While all disambiguation problems canbe tackled in a common framework, most approaches are developed for a specific task.Like Roth [1998] and Cucerzan and Yarowsky [2002], we take a unified view of disambiguation,and apply our systems to preposition selection (Section 3.5), spelling correction(Section 3.6), and non-referential pronoun detection (Section 3.7). In particular we spenda fair amount of time on non-referential pronoun detection. On each of these applications,our systems outperform traditional web-scale approaches.3.2 Related Work3.2.1 Lexical DisambiguationYarowsky [1994] defines lexical disambiguation as a task where a system must “disambiguatetwo or more semantically distinct word-forms which have been conflated into thesame representation in some medium.” Lapata and Keller [2005] divide disambiguationproblems into two groups: generation and analysis. In generation, the confusable candidatesare actual words, like among and between. Generation problems permit learning with1 Google recently announced they are now indexing over 1 trillion unique URLs (http://googleblog.blogspot.com/2008/07/we-knew-web-was-big.html). This figure represents a staggering amount oftextual data.36
Page 1 and 2: University of AlbertaLarge-Scale Se
Page 5 and 6: Table of Contents1 Introduction 11.
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Page 9 and 10: List of Figures2.1 The linear class
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Page 13 and 14: (2) “He saw the trophy won yester
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Page 21 and 22: Chapter 2Supervised and Semi-Superv
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Page 39 and 40: Learning with Natural Automatic Exa
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Page 61 and 62: 807876F-Score747270Stemmed patterns
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DSP uses these labels to identify o
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Semantic classesMotivated by previo
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empirical Pr(n|v) in Equation (6.2)
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Verb Plaus./Implaus. Resnik Dagan e
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SystemAccMost-Recent Noun 17.9%Maxi
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Chapter 7Alignment-Based Discrimina
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ious measures to learn the recurren
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how labeled word pairs can be colle
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Figure 7.1: LCSR histogram and poly
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0.711-pt Average Precision0.60.50.4
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Fr-En Bitext Es-En Bitext De-En Bit
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Chapter 8Conclusions and Future Wor
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8.3 Future WorkThis section outline
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My focus is thus on enabling robust
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[Bergsma and Cherry, 2010] Shane Be
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[Church and Mercer, 1993] Kenneth W
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[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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