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

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“Natural Automatic Examples,” as described in Chapter 2, Section 2.5.4. In analysis, wedisambiguate semantic labels, such as part-of-speech tags, representing abstract propertiesof surface words. For these problems, we have historically needed manually-labeled data.For generation tasks, a model of each candidate’s distribution in text is created. Themodels indicate which usage best fits each context, enabling candidate disambiguation intasks such as spelling correction [Golding and Roth, 1999], preposition selection [Chodorowet al., 2007; Felice and Pulman, 2007], and diacritic restoration [Yarowsky, 1994]. Themodels can be large-scale classifiers or standard N-gram language models (LMs).An N-gram is a sequence of words. A unigram is one word, a bigram is two words, atrigram is three words, and so on. An N-gram language model is a model that computesthe probability of a sentence as the product of the probabilities of the N-grams in the sentence.The (maximum likelihood) probability of an N-gram is simply its count divided bythe number of times it occurs in the corpus. Higher probability sentences will thus be composedof N-grams that are more frequent. For resolving confusable words, we could selectthe candidate that results in a higher whole-sentence probability, effectively combining thecounts of N-grams at different positions.The power of an N-gram language model crucially depends on the data from whichthe counts are taken: the more data, the better. Trigram LMs have long been used forspelling correction, an approach sometimes referred to as the Mays, Damerau, and Mercermodel [Wilcox-O’Hearn et al., 2008]. Gamon et al. [2008] use a Gigaword 5-gram LMfor preposition selection. While web-scale LMs have proved useful for machine translation[Brants et al., 2007], most web-scale disambiguation approaches compare specific sequencecounts rather than full-sentence probabilities. Counts are usually gathered using an Internetsearch engine [Lapata and Keller, 2005; Yi et al., 2008].In analysis problems such as part-of-speech tagging, it is not as obvious how a LM canbe used to score the candidates, since LMs do not contain the candidates themselves, onlysurface words. However, large LMs can also benefit these applications, provided there aresurface words that correlate with the semantic labels. Essentially, we devise some surrogatesfor each label, and determine the likelihood of these surrogates occurring with thegiven context. For example, Mihalcea and Moldovan [1999] perform sense disambiguationby creating label surrogates from similar-word lists for each sense. To choose the sense ofbass in the phrase “caught a huge bass,” we might consider tenor, alto, and pitch for senseone and snapper, mackerel, and tuna for sense two. The sense whose group has the higherweb-frequency count in bass’s context is chosen. [Yu et al., 2007] use a similar approach toverify the near-synonymy of the words in the sense pools of the OntoNotes project [Hovyet al., 2006]. They check whether a word can be substituted into the place of another elementin its sense pool, using a few sentences where the sense pool of the original elementhas been annotated. The substitution likelihood is computed using the counts of N-gramsof various orders from the Google web-scale N-gram corpus (discussed in the followingsubsection).We build on similar ideas in our unified view of analysis and generation disambiguationproblems (Section 3.3). For generation problems, we gather counts for each surfacecandidate filling our 2-to-5-gram patterns. For analysis problems, we use surrogates as thefillers. We collect our pattern counts from a web-scale corpus.37
3.2.2 Web-Scale Statistics in NLPExploiting the vast amount of data on the web is part of a growing trend in natural languageprocessing [Keller and Lapata, 2003]. In this section, we focus on some research that hashad a particular influence on our own work. We begin by discussing approaches that extractinformation using Internet search-engines, before discussing recent approaches that havemade use of the Google web-scale N-gram corpus.There were initially three main avenues of research that used the web as a corpus; allwere based on the use of Internet search engines.In the first line of research, search-engine page counts are used as substitutes for countsof a phrase in a corpus [Grefenstette, 1999; Keller and Lapata, 2003; Chklovski and Pantel,2004; Lapata and Keller, 2005]. That is, a phrase is issued to a search engine as a query,and the count, given by the search engine, of how many pages contain that query is takenas a substitute for the number of times that phrase occurs on the web. Quotation marks areplaced around the phrase so that the words are only matched when they occur in their exactphrasal order. By using Internet-derived statistics, these approaches automatically benefitfrom the growing size and variety of documents on the world wide web. We previouslyused this approach to collect pattern counts that indicate the gender of noun phrases; thisprovided very useful information for an anaphora resolution system [Bergsma, 2005]. Wealso previously showed how a variety of search-engine counts can be used to improve theperformance of search-engine query segmentation [Bergsma and Wang, 2007] (a problemclosely related to Noun-Compound Bracketing, which we explore in Chapter 5).In another line of work, search engines are use to assess how often a pair of wordsoccur on the same page (or how often they occur close to each other), irrespective of theirorder. Thus the page counts returned by a search engine are taken at face value as documentco-occurrence counts. Applications in this area include determining the phrasal semanticorientation (good or bad) for sentiment analysis [Turney, 2002] and assessing the coherenceof key phrases [Turney, 2003].A third line of research involves issuing queries to a search engine and then makinguse of the returned documents. Resnik [1999] shows how the web can be used to gatherbilingual text for machine translation, while Jones and Ghani [2000] use the web to buildcorpora for minority languages. Ravichandran and Hovy [2002] process returned web pagesto identify answer patterns for question answering. In an answer-typing system, Pinchakand Bergsma [2007] use the web to find documents that provide information on unit typesfor how-questions. Many other question-answering systems use the web to assist in findinga correct answer to a question [Brill et al., 2001; Cucerzan and Agichtein, 2005; Radev etal., 2001]. Nakov and Hearst [2005a; 2005b] use search engines both to return counts forN-grams, and also to process the returned results to extract information not available froma search-engine directly, such as punctuation and capitalization.While a lot of progress has been made using search engines to extract web-scale statistics,there are many fundamental issues with this approach. First of all, since the webchanges every day, the results using a search engine are not exactly reproducible. Secondly,some have questioned the reliability of search engine page counts [Kilgarriff, 2007].Most importantly, using search engines to extract count information is terribly inefficient,and thus search engines restrict the number of queries one can issue to gather web-scaleinformation. With limited queries, we can only use limited information in our systems.A solution to these issues was enabled by Thorsten Brants and Alex Franz at Googlewhen they released the Google Web 1T 5-gram Corpus Version 1.1 in 2006 [Brants and38
Page 1 and 2: University of AlbertaLarge-Scale Se
Page 5 and 6: Table of Contents1 Introduction 11.
Page 7 and 8: 7 Alignment-Based Discriminative St
Page 9 and 10: List of Figures2.1 The linear class
Page 11 and 12: drawn in by establishing a partial
Page 13 and 14: (2) “He saw the trophy won yester
Page 15 and 16: actual sentence said, “My son’s
Page 17 and 18: Uses Web-Scale N-grams Auto-Creates
Page 19 and 20: spelling correction, and the identi
Page 21 and 22: Chapter 2Supervised and Semi-Superv
Page 23 and 24: emphasis on “deliverables and eva
Page 25 and 26: Figure 2.1: The linear classifier h
Page 27 and 28: The above experimental set-up is so
Page 29 and 30: and discriminative models therefore
Page 31 and 32: 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: One common disambiguation task is t
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 and 86: System IN O1 O2Baseline 66.9 44.6 6
Page 87 and 88: 90% of the time in Gutenberg. The L
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
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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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