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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Ling 581 advanced computational linguistics

LING 581: Advanced Computational Linguistics

Lecture Notes

February 16th


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


Training data

Training Data


Training data1

Training Data


An experiment

An experiment


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 permutation

Rank of Best Parse for Sentence Permutation

Sentence


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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