LING Intro to Computer Speech and Language Lecture by mikesanye


									Introduction to Computational

Lecture 1: Intro to Field, History, Quick Review of
Regular Expressions, Start of Finite Automata

Based on Dan Jurafsky’s Lecture Notes for the textbook, Speech and Language

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Overview and history of the field
   Knowledge of language
   The role of ambiguity
   Models and Algorithms
   Eliza, Turing, and conversational agents
   History of speech and language processing
Regular expressions

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Computer Speech and Language

 What is Natural Language Processing?
 Getting computers to perform useful tasks involving
 human languages whether for
 Enabling human-machine communication
   information retrieval, question answer, chatbot
 Improving human-human communication
   writing, translation
 Manipulating language objects
   words, phrases, sentences

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Kinds of knowledge needed?

 Consider the following interaction with HAL the
 computer from 2001: A Space Odyssey

 Dave: Open the pod bay doors, Hal.
 HAL: I’m sorry Dave, I’m afraid I can’t do that.

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Knowledge needed to build HAL?

 Speech recognition and synthesis
    Dictionaries (how words are pronounced)
    Phonetics (how to recognize/produce each sound of English)
 Natural language understanding
    Knowledge of the English words involved
     – What they mean
     – How they combine (what is a `pod bay door’?)
    Knowledge of syntactic structure
     – I’m I do, Sorry that afraid Dave I’m can’t

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What’s needed?

 Dialog and pragmatic knowledge (對話與語用知識)
    “open the door” is a REQUEST (as opposed to a
    STATEMENT or information-question)
 Politeness consideration
    It is polite to pretend to want to be cooperative
     – I’m afraid, I can’t…
 Anaphoric Reference
    What is `that’ in `I can’t do that’?
 More practical and realistic situation
    A system to book airline flights
    Also needs much of this kind of knowledge

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Modern conversational agents

 Switchboard Receptionists
    Implications: need to know how a native or non-native speaker of
    English is likely to pronounce a random American name. Eek.
 Airline and other simple dialogue systems
    Lots of fabulously fun pragmatic issues
      – Grounding
      – Error handling
 In general
    Now that there are commercial installations
    Big incentive to use linguistic or any other kind of knowledge if it
    actually improves performance

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Eliza: Weizenbaum (1966)

 User: You are like my father in some ways.
 ELIZA: What resemblance do you see
 User: You are not very aggressive but I think you don’t want me
 to notice that.
 ELIZA: What makes you think I am not aggressive
 User: You don’t argue with me
 ELIZA: Why do you think I don’t argue with you
 User: You are afraid of me
 ELIZA: Does it please you to believe I am afraid of you

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Question Answering

 What does “door” mean?
 What year was Abraham Lincoln born?
 How many states were in the United States when
 Lincoln was born?
 Was there a military draft during the Hoover
 What do US scientists think about whether human
 cloning should be legal?

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Modern QA systems

 Still in infancy
 Simple factoid questions beginning to work OK
 Annual government-sponsored “bakeoff” called TREC

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Machine Translation

 Chinese gloss: Dai-yu alone on bed top think-of-with-gratitude
 Bao-chai Aagain listen to window outside bamboo tip plantain
 leaf of on-top rain sound sigh drop clear cold penetrate curtain
 not feeling again fall down tears come
 Hawkes translation: As she lay there alone, Dai-yu’s thoughts
 turned to Bao-chai… Then she listened to the insistent rustle of
 the rain on the bamboos and plantains outside her window. The
 coldness penetrated the curtains of her bed. Almost without
 noticing it she had begun to cry.

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Machine Translation

 The Story of the Stone
    =The Dream of the Red Chamber (Cao Xueqin 1792)
 Issues: (“Language Divergences”)
    Sentence segmentation
    Coding of tense/aspect
            Penetrate -> penetrated
    Stylistic differences across languages
      – Bamboo tip plaintain leaf -> bamboos and plantains
    Cultural knowledge
      – Curtain -> curtains of her bed

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Open MT Evaluation 2008

    <doc docid="AFP_CMN_20070702.0022" genre="text"


    <seg id="1">白宮促儘快派核檢員監督北韓關閉核反應爐</seg>



    <seg id="2">白宮今天呼籲儘快派遣核檢人員,以監督北韓關閉其

    </p> …
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Open MT Evaluation 2008

    <doc docid="AFP_CMN_20070702.0022">
    <hl><seg id="1">White House Pushes for Nuclear
    Inspectors to Be Sent as Soon as Possible to Monitor North
    Korea's Closure of Its Nuclear Reactors</seg></hl><p>
    <seg id="2">The White House today called for nuclear
    inspectors to be sent as soon as possible to monitor North
    Korea's closure of its nuclear reactors. The White House
    made this call after US President Bush had telephone
    conversations with South Korean President Roh Moo-
    hyun.</seg> ….
    <seg id="6">Hill, the US envoy to the six-party talks, said
    after a visit to Pyongyang last week that he expected the
    Yongbyon nuclear reactors would be shut down in the
    middle of July.</seg>

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 Language is full of ambiguity at all levels
    Toke boundary: ice creem vs. I screem
    Part of speech: walk as verb vs. noun
    Word sense ambiguity: money bank vs river bank
 Fundamental problem of computational linguistics
 Resolving ambiguity is a crucial goal
 Example: Find at least 5 meanings of this sentence:
    I made her duck

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 Find at least 5 meanings of this sentence:
    I made her duck
 I cooked waterfowl for her benefit (to eat)
 I cooked waterfowl belonging to her
 I created the (plaster?) duck she owns
 I caused her to quickly lower her head or body
 I waved my magic wand and turned her into undifferentiated
 At least one other meaning that’s inappropriate for gentle

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Ambiguity is Pervasive

 I caused her to quickly lower her head or body
    Lexical category: “duck” can be a N or V
 I cooked waterfowl belonging to her.
    Lexical category: “her” can be a possessive (“of her”)
    or dative (“for her”) pronoun
 I made the (plaster) duck statue she owns
    Lexical Semantics: “make” can mean “create” or

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Ambiguity is Pervasive

 Grammar: Make can be:
   Transitive: (verb has a noun direct object)
     – I cooked [waterfowl belonging to her]
   Ditransitive: (verb has 2 noun objects)
     – I made [her] (into) [undifferentiated waterfowl]
   Action-transitive (verb has a direct object and
   another verb)
   I caused [her] [to move her body]

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Ambiguity is Pervasive

    I mate or duck
    I’m eight or duck
    Eye maid; her duck
    Aye mate, her duck
    I maid her duck
    I’m aid her duck
    I mate her duck
    I’m ate her duck
    I’m ate or duck
    I mate or duck

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Models and Algorithms

 Models: formalisms used to capture the various kinds of
 linguistic structure.
    State machines (FSA, FS Transducers, Markov models)
    Formal rule systems
      – Context-Free Grammars, Feature-based Grammars)
    Logic (predicate calculus, inference)
    Probabilistic versions of all of these + others
      – Gaussian Mixture Models, Probabilistic relational models, etc
 Algorithms used to manipulate representations to create
    Search (A*, dynamic programming)
    Supervised learning, etc etc

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Language, thought, and
Machine Understanding
 A Gedanken Experiment: Turing Test
 Question “can a machine think” is not operational.
 Operational version:
    2 people and a computer
    Interrogator talks to contestant and computer via teletype
    Task of machine is to convince interrogator it is human
    Task of contestant is to convince interrogator she and not
    machine is human.

Wikepedia: A thought experiment (from the German term
Gedankenexperiment, coined by Hans Christian Ørsted) in
the broadest sense is the use of a hypothetical scenario to
help us understand the way things actually are.

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Eliza: Weizenbaum (1966)

 User: You are like my father in some ways.
 ELIZA: What resemblance do you see
 User: You are not very aggressive but I think you don’t want me
 to notice that.
 ELIZA: What makes you think I am not aggressive
 User: You don’t argue with me
 ELIZA: Why do you think I don’t argue with you
 User: You are afraid of me
 ELIZA: Does it please you to believe I am afraid of you

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History: foundational insights
    Turing 1936: Turing Machine (the most powerful machine
    in abstract terms)
    McCulloch-Pitts neuron (1943)
    Kleene (1951/1956): FSA recognizes a regular language.
    Shannon (1948) link between automata and Markov models
    Chomsky (1956)/Backus (1959)/Naur(1960): CFG
 Probabilistic/Information-theoretic models
    Shannon (1948)
    Bell Labs speech recognition (1952)

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History: the two camps:
     Zellig Harris 1958 TDAP first parser?
       – Cascade of finite-state transducers
     Chomsky: Generative Grammar
     AI workshop at Dartmouth (McCarthy, Minsky, Shannon,
     Newell and Simon: Logic Theorist, General Problem Solver
     Bledsoe and Browning (1959): Bayesian OCR
     Mosteller and Wallace (1964): Bayesian authorship attribution
     Denes (1959): ASR combining grammar and acoustic probability

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Four paradigms:
     Hidden Markov Model 1972
       – Independent application of Baker (CMU) and Jelinek/Bahl/Mercer lab (IBM) following
         work of Baum and colleagues at IDA
     Colmerauer (1970,1975) Q-systems
     Definite Clause Grammars (Pereira and Warren 1980)
     Kay (1979) functional grammar, Bresnan and Kaplan (1982) unification
 Natural language understanding
     Winograd (1972) Shrdlu
     Schank and Abelson (1977) scripts, story understanding
     Influence of case-role work of Fillmore (1968) via Simmons (1973), Schank.
 Discourse Modeling
     Grosz and colleagues: discourse structure and focus
     Perrault and Allen (1980) BDI model

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Empiricism and Finite State
Machines return: 1983-1993
 Finite State Models
    Kaplan and Kay (1981): Phonology/Morphology
    Church (1980): Syntax
 Return of Probabilistic Models:
    Corpora created for language tasks
    Early statistical versions of NLP applications (parsing,
    tagging, machine translation)
    Increased focus on methodological rigor:
     – Can’t test your hypothesis on the data you used to build it!
     – Training sets and test sets

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The field comes together: 1994-
 NLP has borrowed statistical modeling from speech
 recognition, is now standard:
   ACL conference:
    – 1990: 39 articles   1 statistical
    – 2003 62 articles    48 statistical
   Machine learning techniques key
 NLP has borrowed focus on web and search and
 “bag of words models” from information retrieval
 Unified field:
   NLP, MT, ASR, TTS, Dialog, IR

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How this course fits in

 This is our new introductory course in natural
 language processing
 Covers applications
   information retrieval
   machine translation
   educational application
 For speech, and dialog processing, check other
 courses by 張智星

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Some brief demos

 Machine Translation

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Regular expressions

A formal language for specifying text strings
How can we search for any of these?

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          Figure from Dorr/Monz slides
Regular Expressions

  Basic regular expression patterns
  Perl-based syntax (slightly different from other
  notations for regular expressions)
  Disjunctions /[wW]oodchuck/

         Slide from Dorr/Monz     CS 563100NLP Spring 2008   31
Regular Expressions

 Ranges [A-Z]

• Negations [^Ss]

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          Slide from Dorr/Monz
Regular Expressions

    Optional characters ? ,* and +
       ? (0 or 1)
         – /colou?r/  color or colour
       * (0 or more)
         – /oo*h!/  oh! or Ooh! or Ooooh!

     – + (1 or more)
         • /o+h!/  oh! or Ooh! or Ooooh!

                                                            Stephen Cole Kleene
   Wild cards .
    - /beg.n/  begin or began or begun

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           Slide from Dorr/Monz
Regular Expressions

       Anchors ^ and $
           /^[A-Z]/  “Ramallah, Palestine”
           /^[^A-Z]/  “¿verdad?” “really?”
           /\.$/  “It is over.”
           /.$/  ?
       Boundaries \b and \B
           /\bon\b/  “on my way” “Monday”
           /\Bon\b/  “automaton”

       Disjunction |
           /yours|mine/  “it is either yours or mine”

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  Slide from Dorr/Monz
Disjunction, Grouping,
 Column 1 Column 2 Column 3 …
 How do we express this?
 /Column [0-9]+ */
 /(Column [0-9]+ +)*/
    Parenthesis           ()
    Counters              * + ? {}
    Sequences and anchors    the ^my end$
    Disjunction           |
 REs are greedy!

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   Slide from Dorr/Monz

 Find me all instances of the word “the” in a text.
     Misses capitalized examples
     – Returns other or theology

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               Slide from Dorr/Monz

 The process we just went through was based on two
 fixing kinds of errors
   Matching strings that we should not have matched
   (there, then, other)
    – False positives
   Not matching things that we should have matched
    – False negatives

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Errors cont.

We’ll be telling the same story for many tasks, all
quarter. Reducing the error rate for an application often
involves two antagonistic efforts:
   Increasing accuracy (minimizing false positives)
   Increasing coverage (minimizing false negatives).

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More complex RE example

 Regular expressions for prices
   Doesn’t deal with fractions of dollars
   Doesn’t allow $199, not word-aligned

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Advanced operators

             should be _

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     Slide from Dorr/Monz
Overview and history of the field
   Knowledge of language
   The role of ambiguity
   Models and Algorithms
   Eliza, Turing, and conversational agents
   History of computational linguistics
    – The merger of 4 fields: NLP, Speech Recognition, Dialog,
      Information Retrieval
Regular expressions
Finite State Automata

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