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

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Appendix APenn Treebank Tag SetTag Description Examples$ dollar $ -$ –$ A$ C$ HK$ M$ NZ$ S$ U.S.$ US$“ opening quotation mark ‘ “” closing quotation mark ’ ”( opening parenthesis ( [{) closing parenthesis ) ]}, comma ,– dash –. sentence terminator . ! ?: colon or ellipsis : ; ...CC conjunction, coordinating’n and both but either et for less minus neither nor or plus so thereforetimes v. versus vs. whether yetCD numeral, cardinal mid-1890 nine-thirty forty-two one-tenth ten million 0.5 one fortyseven1987 twenty ’79 zero two 78-degrees eighty-four IX ’60s .025fifteen 271,124 dozen quintillion DM2,000 ...DT determiner all an another any both del each either every half la many much naryneither no some such that the them these this thoseEX existential there thereFW foreign word gemeinschaft hund ich jeux habeas Haementeria Herr K’ang-si vouslutihaw alai je jour objets salutaris fille quibusdam pas trop Monteterram fiche oui corporis ...INJJpreposition or conjunction,subordinatingadjective or numeral, ordinalastride among uppon whether out inside pro despite on by throughoutbelow within for towards near behind atop around if like until belownext into if beside ...third ill-mannered pre-war regrettable oiled calamitous first separableectoplasmic battery-powered participatory fourth still-to-be-namedmultilingual multi-disciplinary ...JJR adjective, comparative bleaker braver breezier briefer brighter brisker broader bumper busiercalmer cheaper choosier cleaner clearer closer colder commonercostlier cozier creamier crunchier cuter ...JJS adjective, superlative calmest cheapest choicest classiest cleanest clearest closest commonestcorniest costliest crassest creepiest crudest cutest darkest deadliestdearest deepest densest dinkiest ...LS list item marker A A. B B. C C. D E F First G H I J K One SP-44001 SP-44002 SP-44005 SP-44007 Second Third Three Two a b c d first five four onesix three twoMD modal auxiliary can cannot could couldn’t dare may might must need ought shallshould shouldn’t will wouldNN noun, common, singularor masscommon-carrier cabbage knuckle-duster Casino afghan shed thermostatinvestment slide humour falloff slick wind hyena override subhumanitymachinist ...From: http://www.comp.leeds.ac.uk/ccalas/tagsets/upenn.html127
NNP noun, proper, singular Motown Venneboerger Czestochwa Ranzer Conchita TrumplaneChristos Oceanside Escobar Kreisler Sawyer Cougar Yvette ErvinODI Darryl CTCA Shannon A.K.C. Meltex Liverpool ...NNPS noun, proper, plural Americans Americas Amharas Amityvilles Amusements Anarcho-Syndicalists Andalusians Andes Andruses Angels Animals AnthonyAntilles Antiques Apache Apaches Apocrypha ...NNS noun, common, plural undergraduates scotches bric-a-brac products bodyguards facetscoasts divestitures storehouses designs clubs fragrances averages subjectivistsapprehensions muses factory-jobs ...PDT pre-determiner all both half many quite such sure thisPOS genitive marker ’ ’sPRP pronoun, personal hers herself him himself hisself it itself me myself one oneself oursourselves ownself self she thee theirs them themselves they thou thyusPRP$ pronoun, possessive her his mine my our ours their thy yourRB adverb occasionally unabatingly maddeningly adventurously professedlystirringly prominently technologically magisterially predominatelyswiftly fiscally pitilessly ...RBR adverb, comparative further gloomier grander graver greater grimmer harder harsherhealthier heavier higher however larger later leaner lengthier lessperfectlylesser lonelier longer louder lower more ...RBS adverb, superlative best biggest bluntest earliest farthest first furthest hardest heartiesthighest largest least less most nearest second tightest worstRP particle aboard about across along apart around aside at away back beforebehind by crop down ever fast for forth from go high i.e. in into justlater low more off on open out over per pie raising start teeth thatthrough under unto up up-pp upon whole with youSYM symbol % & ’ ” ”. ) ). * + ,. ¡ = ¿ A[fj] U.S U.S.S.RTO “to” as preposition or infinitivetomarkerUH interjection Goodbye Goody Gosh Wow Jeepers Jee-sus Hubba Hey Kee-reistOops amen huh howdy uh dammit whammo shucks heck anywayswhodunnit honey golly man baby diddle hush sonuvabitch ...VB verb, base form ask assemble assess assign assume atone attention avoid bake balkanizebank begin behold believe bend benefit bevel beware bless boilbomb boost brace break bring broil brush build ...VBD verb, past tense dipped pleaded swiped regummed soaked tidied convened halted registeredcushioned exacted snubbed strode aimed adopted belied figgeredspeculated wore appreciated contemplated ...VBGverb, present participle orgerundtelegraphing stirring focusing angering judging stalling lactating hankerin’alleging veering capping approaching traveling besieging encryptinginterrupting erasing wincing ...VBN verb, past participle multihulled dilapidated aerosolized chaired languished panelizedused experimented flourished imitated reunifed factored condensedsheared unsettled primed dubbed desired ...VBPVBZverb, present tense, not3rd person singularverb, present tense, 3rdperson singularpredominate wrap resort sue twist spill cure lengthen brush terminateappear tend stray glisten obtain comprise detest tease attract emphasizemold postpone sever return wag ...bases reconstructs marks mixes displeases seals carps weavessnatches slumps stretches authorizes smolders pictures emergesstockpiles seduces fizzes uses bolsters slaps speaks pleads ...WDT WH-determiner that what whatever which whicheverWP WH-pronoun that what whatever whatsoever which who whom whosoeverWP$ WH-pronoun, possessive whoseWRB Wh-adverb how however whence whenever where whereby whereever whereinwhereof why128
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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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Accuracy (%)10090807060SUPERLMSUMLM
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We also follow Carlson et al. [2001
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Set BASE [Golding and Roth, 1999] T
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pronoun (#3) guarantees that at the
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807876F-Score747270Stemmed patterns
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anaphoricity by [Denis and Baldridg
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ter, we present a simple technique
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We seek weights such that the class
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each optimum performance is at most
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We now show that ¯w T (diag(¯p)
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Training ExamplesSystem 10 100 1K 1
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Since we wanted the system to learn
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Chapter 5Creating Robust Supervised
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§ In-Domain (IN) Out-of-Domain #1
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Adjective ordering is also needed i
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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
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: [Wang et al., 2008] Qin Iris Wang,
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Large-Scale Semi-Supervised Learning for Natural Language ...

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