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Natural Language Processing for the Web

Natural Language Processing for the Web. Prof. Kathleen McKeown 722 CEPSR, 939-7118 Office Hours: Wed, 1-2; Tues 4-5 TA: Yves Petinot 719 CEPSR, 939-7116 Office Hours: Thurs 12-1, 8-9. Projects. Interim results due next Thursday (3/11) Hand in via courseworks (by midnight )

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Natural Language Processing for the Web

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  1. Natural Language Processing for the Web Prof. Kathleen McKeown 722 CEPSR, 939-7118 Office Hours: Wed, 1-2; Tues 4-5 TA: Yves Petinot 719 CEPSR, 939-7116 Office Hours: Thurs 12-1, 8-9

  2. Projects • Interim results due next Thursday (3/11) • Hand in via courseworks (by midnight) • Guest Lecturer next Thursday: Regina Barzilay, MIT • We will meet in the Interschool Lab, 7th Floor CEPSR (by my office)

  3. Today • All papers are on the same problem • Can we rank them considering: • Presentation • Originality • Results

  4. Learning to Recognize Features of Valid Entailment • Typical approach • Aligns sentence with hypothesis • Produce all possible alignments and return the best scoring one • Closeness of match determines entailment • Problems with this approach • Assumption of monoticity • Assumption of locality • Confounding of alignment with entailment evaluation

  5. Possible alignments • Sharon warns Arafat could be targeted for assassination • The prime minister targeted for assassination • Space of alignments: O((m+1)n) for a sentence of m nodes and hypothesis of n

  6. Word overlap: 2081 a problem • Upward monoticity problem: 98 a problem • Sharon denies Arafat targeted for assassination • Global phenomena: Dogs barked loudly -> dogs barked. No dogs barked loudly does not • What about 231?

  7. Solution • Alignment first • Between dependency parses • Using Wordnet to identify synonyms and collations plus McCallum’s NE tagger • Don’t worry about quantification, modalities • Machine learning for binary classification • Takes into account context in which alignment occurs

  8. Entailment Classification • Logistic regression • 28 features • Polarity: negation, downward monotone quantifiers (few, no), restricting preps (without, except), superlatives (tallest) • Adjuncts: (loudly) • Antonymy: (rose vs fell) • Modality: • map text to modality: possible, not possible, actual, not actual, necessary, not necessary. • Modality pair: yes, weak yes, don’t know, weak no, no • (not possible -> not actual)? -> yes • (possible -> necessary) -> weak no

  9. Factivity: (tried to, managed to) • Quantifiers: no, some many, most, all • Number,date and time features (can do fuzzy matches) • Alignment features: a good score or not? • Best features: adjunct, dates, modals

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