Large-Scale Semi-Supervised Learning for Natural Language ...
Large-Scale Semi-Supervised Learning for Natural Language ... Large-Scale Semi-Supervised Learning for Natural Language ...
List of Tables1.1 Summary of tasks handled in the dissertation . . . . . . . . . . . . . . . . 82.1 The classifier confusion matrix . . . . . . . . . . . . . . . . . . . . . . . . 193.1 SUMLM accuracy combining N-grams from order Min to Max . . . . . . . 453.2 Context-sensitive spelling correction accuracy on different confusion sets . 483.3 Pattern filler types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493.4 Human vs. computer non-referential it detection . . . . . . . . . . . . . . . 534.1 Accuracy of preposition-selection SVMs. . . . . . . . . . . . . . . . . . . 644.2 Accuracy of spell-correction SVMs. . . . . . . . . . . . . . . . . . . . . . 644.3 Accuracy of non-referential detection SVMs. . . . . . . . . . . . . . . . . 655.1 Data for tasks in Chapter 5 . . . . . . . . . . . . . . . . . . . . . . . . . . 705.2 Number of labeled examples for tasks in Chapter 5 . . . . . . . . . . . . . 705.3 Adjective ordering accuracy . . . . . . . . . . . . . . . . . . . . . . . . . 735.4 Spelling correction accuracy . . . . . . . . . . . . . . . . . . . . . . . . . 765.5 NC-bracketing accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . 795.6 Verb-POS-disambiguation accuracy . . . . . . . . . . . . . . . . . . . . . 816.1 Pseudodisambiguation results averaged across each example . . . . . . . . 896.2 Selectional ratings for plausible/implausible direct objects . . . . . . . . . 926.3 Recall on identification of Verb-Object pairs from an unseen corpus . . . . 926.4 Pronoun resolution accuracy on nouns in current or previous sentence. . . . 947.1 Foreign-English cognates and false friend training examples. . . . . . . . . 997.2 Bitext French-English development set cognate identification 11-pt averageprecision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1037.3 Bitext, Dictionary Foreign-to-English cognate identification 11-pt average7.4precision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Example features and weights for various Alignment-Based Discriminative103classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1057.5 Highest scored pairs by Alignment-Based Discriminative classifier . . . . . 106
List of Figures2.1 The linear classifier hyperplane . . . . . . . . . . . . . . . . . . . . . . . . 162.2 Learning from labeled and unlabeled examples . . . . . . . . . . . . . . . 263.1 Preposition selection learning curve . . . . . . . . . . . . . . . . . . . . . 443.2 Preposition selection over high-confidence subsets . . . . . . . . . . . . . . 453.33.4Context-sensitive spelling correction learning curve . . . . . . . . . . . . .Non-referential detection learning curve . . . . . . . . . . . . . . . . . . .47513.5 Effect of pattern-word truncation on non-referential it detection. . . . . . . 524.1 Multi-class classification for web-scale N-gram models . . . . . . . . . . . 595.1 In-domain learning curve of adjective ordering classifiers on BNC. . . . . . 745.2 Out-of-domain learning curve of adjective ordering classifiers on Gutenberg. 745.3 Out-of-domain learning curve of adjective ordering classifiers on Medline. . 755.4 In-domain learning curve of spelling correction classifiers on NYT. . . . . . 765.5 Out-of-domain learning curve of spelling correction classifiers on Gutenberg. 775.6 Out-of-domain learning curve of spelling correction classifiers on Medline. 775.7 In-domain NC-bracketer learning curve . . . . . . . . . . . . . . . . . . . 795.8 Out-of-domain learning curve of verb disambiguation classifiers on Medline. 816.1 Disambiguation results by noun frequency. . . . . . . . . . . . . . . . . . . 916.2 Pronoun resolution precision-recall on MUC. . . . . . . . . . . . . . . . . 937.1 LCSR histogram and polynomial trendline of French-English dictionary pairs.1027.2 Bitext French-English cognate identification learning curve. . . . . . . . . 104
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List of Figures2.1 The linear classifier hyperplane . . . . . . . . . . . . . . . . . . . . . . . . 162.2 <strong>Learning</strong> from labeled and unlabeled examples . . . . . . . . . . . . . . . 263.1 Preposition selection learning curve . . . . . . . . . . . . . . . . . . . . . 443.2 Preposition selection over high-confidence subsets . . . . . . . . . . . . . . 453.33.4Context-sensitive spelling correction learning curve . . . . . . . . . . . . .Non-referential detection learning curve . . . . . . . . . . . . . . . . . . .47513.5 Effect of pattern-word truncation on non-referential it detection. . . . . . . 524.1 Multi-class classification <strong>for</strong> web-scale N-gram models . . . . . . . . . . . 595.1 In-domain learning curve of adjective ordering classifiers on BNC. . . . . . 745.2 Out-of-domain learning curve of adjective ordering classifiers on Gutenberg. 745.3 Out-of-domain learning curve of adjective ordering classifiers on Medline. . 755.4 In-domain learning curve of spelling correction classifiers on NYT. . . . . . 765.5 Out-of-domain learning curve of spelling correction classifiers on Gutenberg. 775.6 Out-of-domain learning curve of spelling correction classifiers on Medline. 775.7 In-domain NC-bracketer learning curve . . . . . . . . . . . . . . . . . . . 795.8 Out-of-domain learning curve of verb disambiguation classifiers on Medline. 816.1 Disambiguation results by noun frequency. . . . . . . . . . . . . . . . . . . 916.2 Pronoun resolution precision-recall on MUC. . . . . . . . . . . . . . . . . 937.1 LCSR histogram and polynomial trendline of French-English dictionary pairs.1027.2 Bitext French-English cognate identification learning curve. . . . . . . . . 104
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