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LING 581: Advanced Computational Linguistics

LING 581: Advanced Computational Linguistics. Lecture Notes February 16th. Administrivia. Homework presentations today A related experiment … New topic: computational semantics. Homework. WSJ corpus : sections 00 through 24 Training : normally 02-21 (20 sections )

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LING 581: Advanced Computational Linguistics

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  1. LING 581: Advanced Computational Linguistics Lecture Notes February 16th

  2. Administrivia • Homework presentations today • A related experiment … • New topic: computational semantics

  3. Homework • WSJ corpus: sections 00 through 24 • Training: normally 02-21 (20 sections) • concatenate mrg files as input to Bikel Collins train • training is fast • Evaluation: on section 23 • use tsurgeon to get data ready for EVALB • use Bikel Collins parse repeatedly on wsj-23.lsp • 2400+ sentences, parsing is slow • Question: • How does the Bikel Collins vary in precision and recall? if you randomly pick 1, 2, 3 up to 20 sections to do the training with… • Use EVALB • plot precision and recall graphs

  4. Perl Code • Generate training data

  5. Training Data

  6. Training Data

  7. An experiment

  8. Parsing Data • All 5! (=120) permutations of • colorless green ideas sleep furiously .

  9. Parsing Data • The winning sentence was: • Furiously ideas sleep colorless green . • after training on sections 02-21 (approx. 40,000 sentences) sleep takes ADJP object with two heads adverb (RB) furiously modifies noun

  10. Parsing Data • The next two highest scoring permutations were: • Furiously green ideas sleep colorless . • Green ideas sleep furiously colorless . sleep takes NP object sleep takes ADJP object

  11. Parsing Data • (Pereira 2002) compared Chomsky’s original minimal pair: • colorless green ideas sleep furiously • furiously sleep ideas green colorless Ranked #23 and #36 respectively out of 120

  12. Parsing Data But … • graph (next slide) shows how arbitrary these rankings are: • furiously sleep ideas green colorless Vastly outranks • colorless green ideas sleep furiously (and the top 3 sentences) when trained on randomly chosen sections covering 14,188 to 31,616 WSJ PTB sentences

  13. Rank of Best Parse for Sentence Permutation Sentence

  14. Rank of Best Parse for Sentence Permutation • Note also that: • colorless green ideas sleep furiously totally outranks the top 3 (and #36) just briefly on one data point (when trained on 33605 sentences)

  15. New Topic Computational Semantics • (Portner 2005), an intro book titled What is meaning?, asks on page 1 • Why formalize? • can construct precise theories • precise theories are better • “they don’t allow the theorists to fudge the data quite so easily as less precise theories do” • Why implement?

  16. Formalization • Challenge: • we all understand the argument made on the previous page? • How do we formalize it? • Why formalize? • can construct precise theories • precise theories are better • “they don’t allow the theorists to fudge the data quite so easily as less precise theories do”

  17. Implementation • We can implement in Prolog, see next time … (Make sure you have SWI Prolog up and running for next time) • free download from www.swi-prolog.org

  18. Simple past vs. present perfect • (1) Mary received the most votes in the election • (2) Mary has received the most votes in the election • (so) Mary will be the next president • Idea • (1) reports a past event • (2) reports a past event and a current “result” • present perfect • expectation... • (entailment)

  19. Simple past vs. present perfect • (3) Will Mary be able to finish Dos Passos’USA trilogy by the next club meeting? It’s so long! • Well, she has read Remembrance of Things Past, and its even longer • what’s a “result” here? • expectation... • (entailment) • Mary has read a really long book before and therefore...

  20. Meaning • What is a Meaning? • difficult sometimes to pin down precisely • by reference to other words • foreign language: 犬(inu) = “dog”

  21. Meaning • What is a Meaning? • Example: • important • Merriam-Webster (sense 1): • marked by or indicative of significant worth or consequence:valuable in content or relationship

  22. Question • What is a Meaning? • Example: • important • Thesaurus • Text: 1 having great meaning or lasting effect • <the discovery of penicillin was a very important event in the history of medicine> • Synonyms big, consequential, eventful, major, material, meaningful, momentous, significant, substantial, weighty • Related Words decisive, fatal, fateful, strategic; earnest, grave, serious, sincere; distinctive, exceptional, impressive, outstanding, prominent, remarkable; valuable, worthwhile, worthy; distinguished, eminent, great, illustrious, preeminent, prestigious; famous, notorious, renowned; all-important, critical, crucial

  23. Question • What is a Meaning? • Meaning = Concept (or thought or idea) • “dog” maps to DOG • <word> maps to <concept> • Problems • need to provide a concept for every meaningful piece of language • how about expressions “whatever”, “three” • need to map different expressions into same concept • Externalization? Putnam’s twin earth experiment (H2O vs. XYZ) • DOG a shared concept?

  24. Question • What is a Meaning? • Meaning = Concept (or thought or idea) • twin earth experiment • same except H2O = XYZ • “water” refers to H2O • “water” refers to XYZ • identical twins on the two earths don’t mean the same thing by the word “water”

  25. Question • What is a Meaning? • Meaning = Concept (or thought or idea) • Skip the “Meaning = Concept” definition • reason the word “dog” means the same thing for you and me • not that we have the same mental constructs relating to the word • it’s because of our intention to apply the word “dog” to the same things out there in our environment

  26. Truth Conditions • The circle is inside the square • Can draw a picture of scenarios for which the statement is true and the statement is false • truth-conditions different from truth-value

  27. Truth Conditions • The circle is inside the square • Proposition expressed by a sentence is its truth-conditions • i.e. sets of possible worlds (aka situations)

  28. Truth Conditions • The circle is inside the square and the circle is dark • and = set intersection • Mary is a student and a baseball fan

  29. Truth Conditions • Mary and John bought a book • and = set intersection ? are Mary and John sets? how about “and = set union”?

  30. Truth Conditions • The square is bigger than the circle • The circle is smaller than the square • Given these two sentences, evaluate • Synonymous • (what does this mean wrt truth conditions?) • Contrary • Entailment • Tautology

  31. Practice • 1. Does sleep entail snore? • 2. Does snore presuppose sleep? • 3. Given the statement “All crows are black”, give an example of a sentence expressing a tautology involving this statement?

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