CSC 594 Topics in AI

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CSC 594 Topics in AI Applied Natural Language
Processing

• Fall 2009/2010
• 1. Introduction

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What is NLP?

• Natural Language Processing (NLP) is a field in
  Artificial Intelligence (AI) devoted to creating
  computers that use natural language as input
  and/or output.

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NLP involves other disciplines..

• Linguistics
• NLP is also called Computational Linguistics
• Psychology
• Mathematics and Statistics
• Information Theory
• Computer Science

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Machines that Can Speak

• HAL 9000 in 2001 A Space Odyssey

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Machines that Can Speak (cont.)

• C3PO in Star Wars

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But Still a Sci-Fi

• However, as 2001 approached it became clear that
  2001's predictions in computer technology were
  far fetched. Natural language, lip reading,
  planning and plain common sense in computers were
  still the stuff of science fiction.HAL 9000 -
  Wikipedia

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NLP is AI-complete

• The most difficult problems in AI manifest
  themselves in human language phenomena.
• Use of language is the touchstone of intelligent
  behavior.

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Test for Intelligence Turing Test

• Alan Turing (1950) proposed a test of a machine's
  capability to perform human-like conversation.

• A human judge engages in a natural language
  conversation with two other parties, one a human
  and the other a machine if the judge cannot
  reliably tell which is which, then the machine is
  said to pass the test.

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Early Conversational Programs

• ELIZA (by Joseph Weizenbaum), 1966
• A psychotherapist
• No real understanding simple pattern-matching to
  respond to user input -gt canned responses

(my ?x depresses me) (why does your ?x depress
you) 5 (life ?x) (why do you say it ?x) 3 (I
could ?x) (you could ?x) 2 (because ?x) (that is
a good reason) 3 (?x) (tell me more) 0
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gtgt Hello. How do you do. Please state your
problem. gtgt Men are all alike. In what way ? gtgt
They're alwas bugging us about something or
other. I'm not sure I understand you fully. gtgt
They're always bugging us about something or
other. Can you think of a specific example ? gtgt
Well, my boyfriend made me come here. Your
boyfriend made you come here ? gtgt He says I'm
depressed much of the time. I am sorry to hear
that you are depressed.
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Recent Conversational ProgramsLoebner Prize
Chatterbots/Chatbots (1)

• In 1990, Hugh Loebner started an annual Turing
  Test competition.
• Conversational Programs are nowadays called
  Chatterbots (or Chatbots).
• 100,000 to the first bot that judges cannot
  distinguish from a real human in a Turing test
  that includes text, visual, and auditory input.
• The Prize dissolves once the 100,000 prize is
  won.

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Loebner Prize Chatterbots/Chatbots (2)

• Nobody has won the prize yet.

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Real-World Applications of NLP

• NLP can be stand-along applications or components
  embedded in other systems.
• Major NLP applications include
• Machine translation
• Question answering
• Summarization
• Conversational agents

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Commercial World

• Lots of exciting stuff going on

Powerset
Source Jurafsky Martin Speech and Language
Processing
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1. Machine Translation (MT)

• One of the very earliest pursuits in computer
  science (after WWII).
• Basic approaches
• Inter-lingual (rule-based)
• Direct translation (corpus-based) ? morepopular
  these days
• Example Google Translate
• MT engine (based on SYSTRAN system developed in
  EC).

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2. Question Answering

• Finds an answer (not a document) to a question
  typed in as a natural language sentence (not
  keywords).
• Most systems can only answer simple, trivial
  pursuit type questions.
• Example Ask.com
• Some search engines perform limited, phrase-based
  QA, e.g. Google

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3. Text Summarization

• Create a summary of a text or texts.
• Many difficult problems, including
• Paraphrases
• Anaphora (e.g.it, they)

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4. Analyzing Web Documents

• Recently there have been many NLP applications
  which analyze (not just retrieve) weblogs,
  discussion forums, message boards, user groups,
  and other forms of user generated media
• Product marketing information
• Political opinion tracking
• Social network analysis
• Buzz analysis (whats hot, what topics are people
  talking about right now).

Source Jurafsky Martin Speech and Language
Processing
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5. NLP in IR

• Query expansion
• Add synonyms, related words to the query terms to
  improve search results.
• Example Google AdWords tool
• NOTE Stemming is NOT a NLP technique!!!

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Source Marti Hearst, i256, at UC Berkeley
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NLP Tasks

• Those NLP applications require several NLP
  analyses
• Word tokenization
• Sentence boundary detection
• Part-of-speech (POS) tagging
• to identify the part-of-speech (e.g. noun, verb)
  of each word
• Named Entity (NE) recognition
• to identify proper nouns (e.g. names of person,
  location, organization domain terminologies)
• Parsing
• to identify the syntactic structure of a sentence
• Semantic analysis
• to derive the meaning of a sentence

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Different Levels of Language Analysis

• Phonology
• Speech audio signal to phonemes
• Morphology
• Inflection (e.g. I, my, me eat, eats,
  ate, eaten)
• Derivation (e.g. teach, teacher, nominate,
  nominee)
• Syntax
• Part-of-speech (noun, verb, adjective,
  preposition, etc.)
• Phrase structure (e.g. noun phrase, verb phrase)
• Semantics
• Meaning of a word (e.g. book as a bound volume
  or an accounting ledger) or a sentence
• Discourse
• Meaning and inter-relation between sentences

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Why is NLP so hard..?

• Understanding natural languages is hard because
  of inherent ambiguity
• Engineering NLP systems is also hard because
  of
• Huge amount of data resources needed (e.g.
  grammar, dictionary, documents to extract
  statistics from)
• Computational complexity (intractable) of
  analyzing a sentence

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Ambiguity (1)

• Get the cat with the gloves.

Source Marti Hearst, i256, at UC Berkeley
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Ambiguity (2)

• 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

Source Jurafsky Martin Speech and Language
Processing
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Ambiguity (3)

• Some ambiguous headlines
• Juvenile Court to Try Shooting Defendant
• Teacher Strikes Idle Kids
• Kids Make Nutritious Snacks
• Bush Wins on Budget, but More Lies Ahead
• Hospitals are Sued by 7 Foot Doctors

Source Marti Hearst, i256, at UC Berkeley
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Ambiguity is Pervasive

• Phonetics
• I mate or duck
• Im eight or duck
• Eye maid her duck
• Aye mate, her duck
• I maid her duck
• Im aid her duck
• I mate her duck
• Im ate her duck
• Im ate or duck
• I mate or duck

Sound likeI made her duck
Source Jurafsky Martin Speech and Language
Processing
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• Lexical category (part-of-speech)
• duck as a noun or a verb
• Lexical Semantics (word meaning)
• duck as an animal or a plaster duck statue
• Compound nouns
• e.g. dog food, Intelligent design scores
• Syntactic ambiguity
• But semantics can sometimes help disambiguate

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Dealing with Ambiguity

• Four possible approaches
• Formal approaches -- Tightly coupled interaction
  among processing levels knowledge from other
  levels can help decide among choices at ambiguous
  levels.
• Pipeline processing that ignores ambiguity as it
  occurs and hopes that other levels can eliminate
  incorrect structures.
• Probabilistic approaches based on making the most
  likely choices
• Dont do anything, maybe it wont matter

Source Jurafsky Martin Speech and Language
Processing
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The Bottom Line

• Complete NL Understanding (thus general
  intelligence) is impossible.
• But we can make incremental progress.
• Also we have made successes in limited domains.
• But NLP is costly Lots of work and resources
  are needed, but the amount of return is sometimes
  not worth it.

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Statistical Approaches to NLP

• Get large text collections (corpora)
• Compute statistics over the words in those
  collections
• Surprising results
• Getting more data is better than fine-tuning
  algorithms!

Banko Brill 01
Source Marti Hearst, i256, at UC Berkeley
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Sentence Analysis
John ate the cake
Syntactic structure
Semantic structure
(ACTION ingest (ACTOR John-1) (PATIENT food))
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Syntactic Parsing

• The process of deriving the phrase structure of a
  sentence is called parsing.
• The structure (often represented by a
  Context-Free parse tree) is based on the grammar.

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Parsing Algorithms

• Top-down Parsing -- (top-down) derivation
• Bottom-up Parsing
• Chart Parsing
• Earleys Algorithm most efficient, O(n3)
• Left-corner Parsing optimization of Earleys
• and lots more

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(Bottom-up) Chart Parsing
John ate the cake 0 1 2 3 4
Grammar
(11) reduce
(10) reduce
---
(9) reduce
(5) shift 2
---
(2) shift 2
(7) shift 2
(4) shift 2
---
(1) shift 1 John
(3) shift 1 ate
(6) shift 1 the
(8) shift 1 cake
0
1
2
3
4
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Earleys Algorithm
John ate the cake 0 1 2 3 4
Grammar
(12) completor
(1) predictor
(11) completor
(2) scanner John
(3) predictor
(4) predictor
(10) completor
(6) completor
(7) predictor
(5) scanner ate
(9) scanner cake
(8) scanner the
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Demo using my CF parser
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Probabilistic Parsing

• For ambiguous sentences, wed like to know which
  parse tree is more likely than others.
• So we must assign probability to each parse tree
  but how?
• A probability of a parse tree t is
  
  where r is a rule used in t.
• and p(r) is obtained from a (annotated) corpus.

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Partial Parsing

• Parsing fails when the coverage of the grammar is
  not complete but its almost impossible to
  write out all legal syntax (without accepting
  ungrammatical sentences).
• Wed like to at least get pieces even when full
  parsing fails.
• Why not abandon full parsing and aim for partial
  parsing from the start

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Semantic Analysis (1)

• Derive the meaning of a sentence.
• Often applied on the result of syntactic
  analysis.
• John ate the cake.
• NP V NP
• ((action INGEST) syntactic verb
  (actor JOHN-01) syntactic subj
  (object FOOD)) syntactic obj
• To do semantic analysis, we need a (semantic)
  dictionary (e.g. WordNet, http//www.cogsci.prince
  ton.edu/wn/).

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Semantic Analysis (2)

• Semantics is a double-edged sword
• Can resolve syntactic ambiguity
• I saw a man on the hill with a telescope
• I saw a man on the hill with a hat
• But introduces semantic ambiguity
• She walked towards the bank
• But in human sentence processing, we seem to
  resolve both types of ambiguities simultaneously
  (and in linear time as well)

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Demo using my Unification parser