Ling 581 advanced computational linguistics
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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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Administrivia
Administrivia

  • Homework presentations today

  • A related experiment …

  • New topic: computational semantics


Homework
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


Perl code
Perl Code

  • Generate training data





Parsing data
Parsing Data

  • All 5! (=120) permutations of

    • colorless green ideas sleep furiously .


Parsing data1
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


Parsing data2
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


Parsing data3
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


Parsing data4
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



Rank of best parse for sentence permutation1
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)


New topic
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?


Formalization
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”


Implementation
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


Simple past vs present perfect
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)


  • Simple past vs present perfect1
    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...


    Meaning
    Meaning

    • What is a Meaning?

      • difficult sometimes to pin down precisely

      • by reference to other words

      • foreign language: 犬(inu) = “dog”


    Meaning1
    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


    Question
    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


    Question1
    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?


    Question2
    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”


    Question3
    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


    Truth conditions
    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


  • Truth conditions1
    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)


  • Truth conditions2
    Truth Conditions

    • The circle is inside the square and the circle is dark

    • and = set intersection

    • Mary is a student and a baseball fan


    Truth conditions3
    Truth Conditions

    • Mary and John bought a book

    • and = set intersection ?

      are Mary and John sets?

      how about “and = set union”?


    Truth conditions4
    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


    Practice
    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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