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

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Accuracy (%)1009590858075706560SUPERLMSUMLMRATIOLMTRIGRAM100 1000Number of training examplesFigure 3.4: Non-referential detection learning curveother pairs, around the 0.80 considered to be good reliability. These are, perhaps surprisingly,the only it-annotation agreement statistics available for written text. They contrastfavourably with the low agreement for categorizing it in spoken dialog [Müller, 2006].3.7.4 Non-referential Pronoun Detection ResultsMain ResultsFor non-referential pronoun detection, BASE (always choosing referential) achieves 59.4%,while SUPERLM reaches 82.4%. RATIOLM, with no tuned thresholds, performs worst(67.4%), while TRIGRAM (74.3%) and SUMLM (79.8%) achieve reasonable performanceby comparing scores for it and they. All differences are statistically significant (McNemar’stest, p
807876F-Score747270Stemmed patternsTruncated patternsUnaltered patterns681 2 3 4 5 6 7 8 9 10Truncated word lengthFigure 3.5: Effect of pattern-word truncation on non-referential it detection.not directly comparable to the above work because they used a different split of trainingand testing data, and because experiments were conducted with a maximum entropy classifierrather than an SVM. However, this previous work nevertheless provides useful insightsinto the performance of SUPERLM on this task. Full details are available in [Bergsma etal., 2008b]. To analyze the output of our system in greater detail, we now also report on theprecision, recall, and F-score of the classifier (defined in Section 2.3.2).Stemming vs. Simple TruncationSince applying an English stemmer to the context words (Section 3.7.2) reduces the portabilityof the distributional technique, we investigated the use of more portable pattern abstraction.Figure 3.5 compares the use of the stemmer to simply truncating the words in thepatterns at a certain maximum length. Using no truncation (Unaltered) drops the F-Scoreby 4.3%, while truncating the patterns to a length of four only drops the F-Score by 1.4%,a difference which is not statistically significant. Simple truncation may be a good optionfor other languages where stemmers are not readily available. The optimum truncation sizewill likely depend on the length of the base forms of words in that language. For real-worldapplication of our approach, truncation also reduces the table sizes (and thus storage andlook-up costs) of any pre-compiled it-pattern database.A Human StudyWe also wondered, what is the effect of making a classification based solely on, in aggregate,four words of context on either side of it. Another way to view the limited context isto ask, given the amount of context we have, are we making optimum use of it? We answerthis by seeing how well humans can do with the same information. Our system uses 5-gramcontext patterns that together span from four-to-the-left to four-to-the-right of the pronoun.We thus provide these same nine-token windows to our human subjects, and ask them todecide whether the pronouns refer to previous noun phrases or not, based on these contexts.52
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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
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 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: pronoun (#3) guarantees that at the
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
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
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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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