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Computational Linguistics

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Jane said Paul believed Fido barked. Who said Paul believed Fido ... A Review of Skinner's 'Verbal Behaviour' (1959) Together launched Cognitive Revolution' ... – PowerPoint PPT presentation

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Title: Computational Linguistics


1
Computational Linguistics
  • An Introduction

2
Introduction
  • Computational Linguistics within Artificial
    Intelligence
  • Linguistics what it is, past influences
  • Language underlying set of rules
  • Linguistics todays issues and how they impact
    computational linguistics

3
Computational Linguistics
  • sub-field of Artificial Intelligence
  • Natural Language Processing (NLP)
  • used in interfaces, search engines etc.
  • applies techniques developed in Linguistics

4
Artificial Intelligence
  • Connectionism
  • based roughly on our brain neural nets
  • nodes in network
  • network learns - connections strengthen
  • Classical AI
  • symbols plus rules for manipulating symbols
  • NLP
  • words symbols
  • grammar rules rules to manipulate words.

5
Classical AI
  • successful in limited domain
  • (eg Big Blue chess - used brute force)
  • not so good when problems imbedded in world
  • NLP
  • When she hit the nail with the hammer the head
    flew off.
  • requires world knowledge to disambiguate

6
Connectionism
  • node (computing unit)
  • connections between nodes vary in strength
  • learning rule error signal ideal - actual
  • input output error signal

7
Connectionism..some success
  • connectionist net was trained to learn the past
    tense of verbs. (Rummelhart McClelland 1986)
  • followed same U shaped learning pattern of
    children
  • went
  • learn rule over generalized goed
  • master both regular and irregular past tense

8
Connectionismnot solution
  • when children hear a sentence
  • The dog chased the cat.
  • spontaneously treat cat as if belonging to the
    same category as dog
  • neural nets fail to generalize in this way...

9
Unique Challenges
  • not all problems similar to other areas of AI
  • for example early search engines
  • what is a word?
  • computer equals computers but not computation
  • need a deeper analysis of language linguistics

10
Linguistics
  • history
  • evolutionary history of sets of related languages
  • structures
  • the structures that make up language
  • (SVO-English) (SOV-Japanese) (VSO-Arabic)
  • use
  • what do words and sentences mean? Mental
    representations? Function of use?

11
Sub-fields of Linguistics
  • phonetics (sounds in language)
  • where sounds are made
  • phonology (sound patterns in language)
  • what sound combinations are allowed
  • morphology (words and word structures)
  • rules for combining morphemes
  • syntax (sentence and sentence structures)
  • rules for coming words in a sentence
  • semantics (meaning)

12
Grammar
  • descriptive
  • how languages are actually used
  • prescriptive
  • the correct way to speak, based on current
    standards
  • theoretical
  • theories of language structures
  • formalize rules we unconsciously apply when
    speaking

13
Examples of Rules We Apply(From English)
  • Morphology
  • encounter new verb in present tense flig
  • would use it in the past tense fligged
  • Phonology
  • ptil, sorlint, bkat
  • which one could be an English word?

14
Unconscious Rules
  • only sorlint is a candidate for a new English
    word because
  • Phonological Rule in English
  • words cannot begin with two stop consonants
  • p, b, t, d, k, g

15
Linguistics Early 20th Century
  • structuralism classifying/taxanomic
  • Focus on language form (S -gt NP VP) as opposed to
    meaning (S -gt complete thought)
  • Edward Sapir
  • analyzed Amerindian Languages
  • Bloomfield
  • influenced by behaviourism
  • WWII
  • need to analyze and learn many new languages

16
Language Form Underlying System of Rules
  • Phrase Structure Rules
  • Representing sentences tree structures
  • Parts of Speech
  • Constituents and Categories
  • Ambiguity more than one possible way to
    parse sentence

17
Phrase Structure Rules
  • Rules Lexicon
  • S NP VP Det the
  • NP Det N N child
  • VP V V sleeps

18
Representation
  • tree structures used to represent sentences
  • sentences broken down into constituents
  • S
  • NP VP
  • Det N V
  • The child sleeps

19
Parts of Speech
  • NOUNS
  • person, animal, place, quality, idea
  • Mary, university, thought, childhood
  • PRONOUN
  • refer to, or stand in for, a noun
  • everyone, she, they
  • VERB
  • action, state of being
  • walk, to be (is, are), know

20
Parts of Speech
  • ADJECTIVE
  • describes or modifies a noun
  • happy, red, many (Many people like chocolate)
  • ADVERB
  • modifies a verb, adjective, adverb, phrase,
    clause, sentence
  • slowly, very slowly, immediately (Immediately,
    they...)
  • PREPOSITION
  • links nouns, pronouns, and phrases to other parts
    of the sentence - express relationships in time
    or space between things and events
  • to, before, beside, on, at, of, under, since,
    after.

21
Parts of Speech
  • CONJUNCTION
  • joins two or more words, phrases, or clauses
  • and, but, or
  • INTERJECTION
  • used to protest, exclaim, command
  • oh, wow, hey
  • DETERMINER
  • articles the, a/an (only two in English)
  • demonstratives (modifies noun, indicates
    position of something in relation to speaker)
    that, those, this, these

22
Constituents and Categories
  • Categories parts of speech
  • (nouns, verbs, etc.)
  • Constituents words that hang together
  • can move together
  • can be deleted together
  • makes sense to talk of the meaning of a
    constituent structure (over other combinations)

23
Constituents and Categories
  • S
  • NP VP
  • det N V NP PP
  • det N P NP
  • det N
  • the dog chased the cat into the
    garden

24
Ambiguity in Phrase Structure
  • ambiguity can result when there is more than one
    possible phrase structure for a sentence
  • Example
  • The girl saw the boy with the telescope.
  • Who has the telescope?
  • The girl? Or the boy?

25
Phrase Structure Rules
  • S NP VP det the
  • NP det N N girl, boy, telescope
  • NP det N PP P with
  • PP P NP V saw
  • VP V
  • VP V NP
  • VP V NP PP

26
Phrase Structure Rules
  • The girl has the telescope.
  • S
  • NP VP
  • det N V NP PP
  • the girl saw
  • det N P NP
  • the boy
    with

  • det N
  • the telescope

27
Phrase Structure Rules
  • The boy has the telescope.
  • S
  • NP VP
  • det N V NP
  • the girl saw
  • D N PP
  • the boy
  • P NP
  • with
  • D N

  • the telescope

28
Phrase Structure Rules Complete Grammar?
  • no
  • many constructions impossible to describe
    adequately

29
Wh movement
  • Jane said Paul believed Fido barked.
  • Who said Paul believed Fido barked? (Jane)
  • Who did Jane say believed Fido barked? (Paul)
  • Who did Jane say Paul believed barked? (Fido)

30
Transformational Rules
  • Wh - movement any amount of structure between
    original position of moved word and where it ends
    up.
  • Phrase structure rules cannot account for it.

31
Transformational-Generative Grammar
  • Noam Chomsky
  • Syntactic Structures (1957)
  • 1 - 100 most influential works in Cognitive
    Science in the 20th Century
  • A Review of Skinners Verbal Behaviour (1959)
  • Together launched Cognitive Revolution
  • mind rules representation
  • descriptive theories to explanatory theories

32
Universal Grammar
  • language is innate
  • each language is an example of UG
  • biological basis of language

33
Universal Grammar Requires
  • descriptive adequacy
  • detailed enough to describe any possible
    construction in any human language
  • explanatory adequacy
  • simple enough to reflect the small set of inborn
    principles that allow humans to acquire and use
    language

34
Tension between two requirements of UG
  • A theory has explanatory adequacy to the extent
    that it is able to show how, from the data
    available to a child, a child can get a
    descriptively adequate grammar.
  • to be more descriptively adequate, need more
    descriptive devices
  • more devices, harder it is for a child to gain
    descriptively adequate grammar from data

35
How does this help computational linguistics?
  • Phrase structure rules inadequate to describe
    language.
  • Transformational rules difficult to parse -
    seldom used in NLP.
  • TG caused explosion in rules.
  • Chomsky has since moved on to minimalist
    program.

36
Minimalist Program
  • two levels of representation
  • phonetic form
  • logical form
  • implication of language being innate - language
    has evolved (not mathematically elegant)
  • does not follow rules perfectlyin some respects
    unusable

37
Is natural language processing possible?
  • One reason for studying language - and for me
    personally the most compelling reason - is that
    it is tempting to regard language, in the
    traditional phrase, as a mirror of mind.
  • Chomsky, 1975
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