Context Free Grammars Reading: Chap 12-13, Jurafsky & Martin
Language Perception and Comprehension - …jurafsky/symbsys100.2005/Lec_5...Language Perception and...
Transcript of Language Perception and Comprehension - …jurafsky/symbsys100.2005/Lec_5...Language Perception and...
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Jurafsky Symbolic Systems 100, Spring 2005
Language Perception andComprehension
May 5, 2005
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Jurafsky Symbolic Systems 100, Spring 2005
Outline
• Reminder: ambiguity and disambiguation• Recognition of phones
– Use of phonetic context– Use of lexical context– Use of visual context
• Lexical Access: recognition of words:– Segmentation– Use of visual information– Word sense disambiguation
• Ambiguity at higher levels
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Jurafsky Symbolic Systems 100, Spring 2005
Reminder of Ambiguity (fromfirst day of class)
• Find at least 5 meanings of this sentence:– I made her duck
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Jurafsky Symbolic Systems 100, Spring 2005
Ambiguity
• 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 waterfowl• At least one other meaning that’s inappropriate
for gentle company.
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Jurafsky Symbolic Systems 100, Spring 2005
Ambiguity is Pervasive
• I caused her to quickly lower her head or body– Grammar: “duck” can be a noun (waterfowl) or a
verb (move body)• I cooked waterfowl belonging to her.
– Grammar: “her” can be a possessive (“of her”) ordative (“for her”) pronoun
• I made the (plaster) duck statue she owns– Meaning: “make” can mean “create” or “cook”
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Jurafsky Symbolic Systems 100, Spring 2005
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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Jurafsky Symbolic Systems 100, Spring 2005
Ambiguity is Pervasive• Phonetics!
– 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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Jurafsky Symbolic Systems 100, Spring 2005
Syntactic Ambiguity
• Grammar:– “The other day I shot an elephant in my
pajamas (what he was doing in pajamas I’llnever know)”
– Groucho Mark
– What’s the ambiguity?• “In my pajamas” can modify “I” or “elephant”
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Jurafsky Symbolic Systems 100, Spring 2005
What we’ve learned
• Ambiguity is pervasive– Phonetics– Segmentation– Word part of speech– Word meaning– Syntactic properties
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Jurafsky Symbolic Systems 100, Spring 2005
How do we deal with ambiguity?
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Jurafsky Symbolic Systems 100, Spring 2005
Language processing is fast
• Shadowing task -Marslen-Wilson (1975)• Some subjects can close-shadow• Can shadow at latencies of 250-275 ms• Less 50-75 ms for response execution
– So ~200 ms to identify a word– Before acoustic offset of word!
• Furthermore, shadowers in this time cancorrect errors in pronunciation or grammar!
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Jurafsky Symbolic Systems 100, Spring 2005
What we’ve learned
• Language perception is very fast
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Jurafsky Symbolic Systems 100, Spring 2005
Speech perception
• Words are made up of units called “phones”• “duck”: [d ah k]• “eat”: [iy t]• “made”: [m ey d]• “her”: [h er]• “I”: [ay]• “symbolic” [s ih m b aa l ih k]• “systems” [s ih s t em z]• English has about 50 (some lgs have less, some
more)
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Jurafsky Symbolic Systems 100, Spring 2005
Phone perception
• People hear sound waves• How are they able to recognize words in the
input?• Assumption: first they recognize the phones that
make up the words• How does phone perception work?
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Jurafsky Symbolic Systems 100, Spring 2005
Phone perception is difficult
• Different people have different accents• People talk fast or slow• Many phones sound alike, are hard to tell
apart• Most important issue: context
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Jurafsky Symbolic Systems 100, Spring 2005
Phones are context-dependent
• http://www.fon.hum.uva.nl/praat/
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Jurafsky Symbolic Systems 100, Spring 2005
Phones are context-dependent
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Jurafsky Symbolic Systems 100, Spring 2005
Warren (1970)
• The state governors met with theirrespective legislatures convening in thecapital city
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Jurafsky Symbolic Systems 100, Spring 2005
Warren (1970): PhonemeRestoration Effect
• The state governors met with their respectivelegislatures convening in the capital city
• The /s/ was deleted in “Legi*latures” andreplaced with a cough.
• Warren found participants– Heard the word normally– Only one participant reported a missing phoneme– (But reported the wrong one!)
• Knowledge about likely spoken word can “fillin” missing phoneme information
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Jurafsky Symbolic Systems 100, Spring 2005
Warren (1970): PhonemeRestoration Effect
1) The *eel was on the axle.2) The *eel was on the shoe.3) The *eel was on the orange.4) The *eel was on the table.• Listeners reported hearing
1) Wheel2) Heel3) Peel4) Meal
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Jurafsky Symbolic Systems 100, Spring 2005
McGurk Effect
http://www.media.uio.no/personer/arntm/McGurk_english.html
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Jurafsky Symbolic Systems 100, Spring 2005
McGurk Effect: an Auditory Illusion
• Visual cues to syllable“ga”• Auditory cues to syllable “ba”• Results in perception of “da” or “tha”
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Jurafsky Symbolic Systems 100, Spring 2005
What we’ve learned
• Phone perception relies on knowledge atdifferent levels to solve problem ofambiguous input.– Phonetic context– Lexical context– Visual context
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Jurafsky Symbolic Systems 100, Spring 2005
Lexical Access:Detection/Recognition of words
• Segmenting words in speech• Use of visual information in word search• Speed of disambiguation process
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Jurafsky Symbolic Systems 100, Spring 2005
Lexical Access: segmentation
• Speech doesn’t come with spaces in it
– The stuffy nose can lead to trouble– The stuff he knows can lead to trouble
– Some others I’ve seen– Some mothers I’ve seen
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Jurafsky Symbolic Systems 100, Spring 2005
Word segmentation: experiment
• Shillcock (1990)• “Cross-modal priming” experiment
– Based on “lexical decision” (LD) task
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Jurafsky Symbolic Systems 100, Spring 2005
Lexical Decision
• Subjects at computer• 2 buttons, YES and NO• See strings of letters on screen• Have to decide “are these a word or not”?
– DOCTOR - yes– DOCPOR - no– THINK - yes– THIFF - no
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Jurafsky Symbolic Systems 100, Spring 2005
Lexical Decision of “FLINK”
http://ibs.derby.ac.uk/~kpat/Israel_cognitive/button1.jpg
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Jurafsky Symbolic Systems 100, Spring 2005
Lexical Decision
• Facts about Lexical Decision– More frequent words are recognized faster– Shorter words are recognized faster– Semantic Priming:
• NURSE• DOCTORFaster to recognize DOCTOR than• PURSE• DOCTOR
– So something about the meaning of NURSE “primes”the recognition of DOCTOR
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Jurafsky Symbolic Systems 100, Spring 2005
Shillcock (1990) Cross-ModalPriming
• Subjects hear a sentence over a headphone.• At some point in the sentence, subjects see a
word on screen and have to do LD1) The scientist made a new discovery last year.2) The scientist made a novel discovery last year
• Lexical decision to NUDIST• Subjects were primed in (1) but not in (2)• Idea: speakers first mis-segmented “new dis” as
NUDIST• But speakers were not aware of having done this
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Jurafsky Symbolic Systems 100, Spring 2005
What we’ve learned
• Word recognition in speech is parallel• Multiple possible segmentations are
considered and rejected subconsciously andquickly
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Jurafsky Symbolic Systems 100, Spring 2005
Use of visual information inlexical access
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Jurafsky Symbolic Systems 100, Spring 2005
Eye tracking example
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Jurafsky Symbolic Systems 100, Spring 2005
The candle/candy task
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Jurafsky Symbolic Systems 100, Spring 2005
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Jurafsky Symbolic Systems 100, Spring 2005
“Pick up the candy”
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Jurafsky Symbolic Systems 100, Spring 2005
Word Recognition
• Word recognition is incremental– Before the end of the word is spoken, eye
movements are launhced to possible targets• Millisecond by millisecond, information
from the visual environment is used in theprocess of word recognition.– Word recognition is fatser when there are no
competitors visible
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Jurafsky Symbolic Systems 100, Spring 2005
Word sense disambiguation
• Words can have two meanings– “bug”
• Recording device• Insecty thing
• Also called “lexical ambiguity” or “wordsense ambiguity”
• How do people resolve lexical ambiguity?
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Jurafsky Symbolic Systems 100, Spring 2005
Swinney (1979)
• Rumor has it that, for years, the governmentbuilding had been plagued with problems.The man was not surprised when he foundseveral spiders, roaches, and other bugs (1)in the corner (2) of his room."
• Immedidately at (1), which sense of the word“bug” is active?– Insect– Recording device– Both– Neither
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Jurafsky Symbolic Systems 100, Spring 2005
Swinney (1979)• Rumor has it that, for years, the government building
had been plagued with problems. The man was notsurprised when he found several spiders, roaches,and other bugs (1) in the corner (2) of his room."
• Cross-Modal Priming• Test words
– ANT (appropriate for the context)– SPY (not appropriate, but related to the other meaning of
bugs)– SEW (unrelated control word)
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Jurafsky Symbolic Systems 100, Spring 2005
Swinney (1979) Results• Immediately: facilitation of both
– ANT (appropriate for the context)– SPY (not appropriate, but related to the other meaning of
bugs)• when compared to
– SEW (unrelated control word)• By 750 millisec later (other studies showed 200 ms)
only find facilitation for ANT• Idea: parallel activation of all meanings, they
compete, by about 200 ms later, only the correct oneis still active, it’s then available to consciousness
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Jurafsky Symbolic Systems 100, Spring 2005
What we’ve learned
• Word meaning recognition is also parallel• Lots of contextual information is used
(very quickly but perhaps not immediately)to resolve lexical ambiguities
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Jurafsky Symbolic Systems 100, Spring 2005
Conversational meaning
• Words mean things• Consciously we know that• But even when we’re not conscious they
still mean things• Conversational disambiguation
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Jurafsky Symbolic Systems 100, Spring 2005
Discourse disambiguation
• We can use sentences in a conversation fordifferent purposes– Question– Command– Statement– Agreement– Disagreement
• These are called “speech acts”• Speech act ambiguity:
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Jurafsky Symbolic Systems 100, Spring 2005
Conclusion• Language is highly ambiguous
– Phone detection (Warren, “stamp”)– Word segmentation (Shillcock)– Word semantics (Swinney)– Grammar (duck)– Pragmatics (Who’s on first)
• Humans resolve by:– consider each interpretation of an ambiguity,– combine visual, lexical, phonetic knowledge/context to choose most
likely meaning,– subconsciously
• Current research: what knowledge sources, how learned, howrepresented, how combined