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CMSC 723 / LING 645: Intro to Computational Linguistics
September 1, 2004: Dorr
Overview, History, Goals, Problems, Techniques; Intro to MT (J&M 1, 21)
Prof. Bonnie J. DorrDr. Christof Monz
TA: Adam Lee
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Administrivia
http://www.umiacs.umd.edu/~christof/courses/cmsc723-fall04/
IMPORTANT:•For Today: Chapters 1 and 21•For Next Time: Chapter 2
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Other Important Stuff
This course is interdisciplinary—cuts across different areas of expertise. Expect that a subset of the class will be learning new material at any time, while others will have to be patient! (The subsets will swap frequently!)
Project 1 and Project 2 are designed differently. Be prepared for this distinction!
– P1 will focus on the fundamentals, getting your feet wet with software. By the end, you should feel comfortable using/testing certain types of NLP software.
– P2 will require a significantly deeper level of understanding, critique, analysis. You’ll be expected to think deeply and write a lot in the second project. What you write will be a major portion of the grade!
No solutions will be handed out. Written comments will be sent to you by the TA.
All email correspondence MUST HAVE “CMSC 723” in the Subject line!!! Submission format for assignments, projects: plain ascii, pdf Assignment 1 will be posted next week.
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CL vs NLP
Why “Computational Linguistics (CL)” rather than “Natural Language Processing” (NLP)? •Computational Linguistics
— Computers dealing with language— Modeling what people do
•Natural Language Processing—Applications on the computer side
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Relation of CL to Other Disciplines
Artificial Intelligence (AI)(notions of rep, search, etc.)
Machine Learning(particularly, probabilistic or statistic ML techniques) CL
Linguistics (Syntax, Semantics, etc.)
Psychology
Electrical Engineering (EE) (Optical Character Recognition)
Philosophy of Language, Formal Logic
InformationRetrieval
Theory of Computation
Human ComputerInteraction (HCI)
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A Sampling of “Other Disciplines”
Linguistics: formal grammars, abstract characterization of what is to be learned.
Computer Science: algorithms for efficient learning or online deployment of these systems in automata.
Engineering: stochastic techniques for characterizing regular patterns for learning and ambiguity resolution.
Psychology: Insights into what linguistic constructions are easy or difficult for people to learn or to use
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History: 1940-1950’s
Development of formal language theory (Chomsky, Kleene, Backus). – Formal characterization of classes of grammar
(context-free, regular)
– Association with relevant automata
Probability theory: language understanding as decoding through noisy channel (Shannon)– Use of information theoretic concepts like entropy to
measure success of language models.
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1957-1983 Symbolic vs. Stochastic
Symbolic– Use of formal grammars as basis for natural language
processing and learning systems. (Chomsky, Harris)– Use of logic and logic based programming for
characterizing syntactic or semantic inference (Kaplan, Kay, Pereira)
– First toy natural language understanding and generation systems (Woods, Minsky, Schank, Winograd, Colmerauer)
– Discourse Processing: Role of Intention, Focus (Grosz, Sidner, Hobbs)
Stochastic Modeling– Probabilistic methods for early speech recognition, OCR
(Bledsoe and Browning, Jelinek, Black, Mercer)
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1983-1993: Return of Empiricism
Use of stochastic techniques for part of speech tagging, parsing, word sense disambiguation, etc.
Comparison of stochastic, symbolic, more or less powerful models for language understanding and learning tasks.
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1993-Present
Advances in software and hardware create NLP needs for information retrieval (web), machine translation, spelling and grammar checking, speech recognition and synthesis.
Stochastic and symbolic methods combine for real world applications.
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Language and Intelligence: Turing Test
Turing test: – machine, human, and human judge
Judge asks questions of computer and human.– Machine’s job is to act like a human, human’s job is
to convince judge that he’s not the machine.
– Machine judged “intelligent” if it can fool judge.
Judgement of “intelligence” linked to appropriate answers to questions from the system.
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ELIZA
Remarkably simple “Rogerian Psychologist”
Uses Pattern Matching to carry on limited form of conversation.
Seems to “Pass the Turing Test!” (McCorduck, 1979, pp. 225-226)
Eliza Demo:http://www.lpa.co.uk/pws_dem4.htm
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What’s involved in an “intelligent” Answer?
Analysis:
Decomposition of the signal (spoken or written) eventually into meaningful units.
This involves …
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Speech/Character Recognition
Decomposition into words, segmentation of words into appropriate phones or letters
Requires knowledge of phonological patterns:– I’m enormously proud.– I mean to make you proud.
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Morphological Analysis
Inflectional– duck + s = [N duck] + [plural s]– duck + s = [V duck] + [3rd person s]
Derivational– kind, kindness
Spelling changes– drop, dropping– hide, hiding
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Syntactic Analysis
Associate constituent structure with stringPrepare for semantic interpretation
S
NP VP
I V NP
watched det N
the terrapin
OR: watch Subject Object
I terrapin Det the
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Semantics
A way of representing meaningAbstracts away from syntactic structureExample:
– First-Order Logic: watch(I,terrapin)– Can be: “I watched the terrapin” or “The
terrapin was watched by me”Real language is complex:
– Who did I watch?
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Lexical Semantics
The Terrapin, is who I watched.Watch the Terrapin is what I do best.*Terrapin is what I watched the
I= experiencerWatch the Terrapin = predicateThe Terrapin = patient
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Compositional Semantics
Association of parts of a proposition with semantic roles
Scoping
Experiencer Predicate: Be (perc)
I (1st pers, sg) pred patient
saw the Terrapin
Proposition
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Word-Governed Semantics
Any verb can add “able” to form an adjective.– I taught the class . The class is teachable– I rejected the idea. The idea is rejectable.
Association of particular words with specific semantic forms.– John (masculine) – The boys ( masculine, plural, human)
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Pragmatics
Real world knowledge, speaker intention, goal of utterance.
Related to sociology.Example 1:
– Could you turn in your assignments now (command)– Could you finish the homework? (question, command)
Example 2:– I couldn’t decide how to catch the crook. Then I decided
to spy on the crook with binoculars.– To my surprise, I found out he had them too. Then I knew
to just follow the crook with binoculars. [ the crook [with binoculars]] [ the crook] [ with binoculars]
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Discourse Analysis
Discourse: How propositions fit together in a conversation—multi-sentence processing.– Pronoun reference:
The professor told the student to finish the assignment. He was pretty aggravated at how long it was taking to pass it in.
– Multiple reference to same entity:George W. Bush, president of the U.S.
– Relation between sentences:John hit the man. He had stolen his bicycle
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NLP Pipeline
Phonetic Analysis
Morphological analysis
OCR/Tokenization
Syntactic analysis
Semantic Interpretation
Discourse Processing
speech text
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Relation to Machine Translation
Morphological analysis
Syntactic analysis
Semantic Interpretation
Interlingua
inputanalysis generation
Morphological synthesis
Syntactic realization
Lexical selection
output
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Ambiguity
I made her duckI made duckling for herI made the duckling belonging to herI created the duck she ownsI forced her to lower her headBy magic, I changed her into a duck
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S S
NP VP NP VP
I V NP VP I V NP
made her V made det N
duck her duck
Syntactic Disambiguation
Structural ambiguity:
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Part of Speech Tagging and Word Sense Disambiguation
[verb Duck ] ! [noun Duck] is delicious for dinner
I went to the bank to deposit my check. I went to the bank to look out at the river. I went to the bank of windows and chose
the one dealing with last names beginning with “d”.
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Resources forNLP Systems
• Dictionary• Morphology and Spelling Rules• Grammar Rules• Semantic Interpretation Rules• Discourse Interpretation
Natural Language processing involves (1) learning or fashioning the rules for each component, (2) embedding the rules in the relevant automaton, (3) and using the automaton to efficiently process the input .
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Some NLP Applications
Machine Translation—Babelfish (Alta Vista):
Question Answering—Ask Jeeves (Ask Jeeves):
Language Summarization—MEAD (U. Michigan):
Spoken Language Recognition— EduSpeak (SRI):
Automatic Essay evaluation—E-Rater (ETS):
Information Retrieval and Extraction—NetOwl (SRA):
http://babelfish.altavista.com/translate.dyn
http://www.ets.org/research/erater.html
http://www.eduspeak.com/
http://www.netowl.com/extractor_summary.html
http://www.ask.com/
http://www.summarization.com/mead
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What is MT?
Definition: Translation from one natural language to another by means of a computerized system
Early failuresLater: varying degrees of success
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An Old Example
The spirit is willing but the flesh is weak
The vodka is good but the meat is rotten
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Machine Translation History
1950’s: Intensive research activity in MT1960’s: Direct word-for-word replacement1966 (ALPAC): NRC Report on MTConclusion: MT no longer worthy of serious
scientific investigation.1966-1975: `Recovery period’1975-1985: Resurgence (Europe, Japan)1985-present: Resurgence (US)
http://ourworld.compuserve.com/homepages/WJHutchins/MTS-93.htm.
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What happened between ALPAC and Now?
Need for MT and other NLP applications confirmed
Change in expectationsComputers have become faster, more powerfulWWWPolitical state of the worldMaturation of LinguisticsDevelopment of hybrid statistical/symbolic
approaches
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Three MT Approaches: Direct, Transfer, Interlingual
Interlingua
SemanticStructure
SemanticStructure
SyntacticStructure
SyntacticStructure
WordStructure
WordStructure
Source Text Target Text
SemanticComposition
SemanticDecomposition
SemanticAnalysis
SemanticGeneration
SyntacticAnalysis
SyntacticGeneration
MorphologicalAnalysis
MorphologicalGeneration
SemanticTransfer
SyntacticTransfer
Direct
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Examples of Three Approaches
Direct:– I checked his answers against those of the teacher →
Yo comparé sus respuestas a las de la profesora
– Rule: [check X against Y] → [comparar X a Y]
Transfer:– Ich habe ihn gesehen → I have seen him
– Rule: [clause agt aux obj pred] → [clause agt aux pred obj]
Interlingual:– I like Mary→ Mary me gusta a mí
– Rep: [BeIdent (I [ATIdent (I, Mary)] Like+ingly)]
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MT Systems: 1964-1990
Direct: GAT [Georgetown, 1964], TAUM-METEO [Colmerauer et al. 1971]
Transfer: GETA/ARIANE [Boitet, 1978]LMT [McCord, 1989], METAL [Thurmair, 1990], MiMo [Arnold & Sadler, 1990], …
Interlingual: MOPTRANS [Schank, 1974], KBMT [Nirenburg et al, 1992], UNITRAN [Dorr, 1990]
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Statistical MT and Hybrid Symbolic/Stats MT: 1990-Present
Candide [Brown, 1990, 1992]; Halo/Nitrogen [Langkilde and Knight, 1998], [Yamada and Knight, 2002]; GHMT [Dorr and Habash, 2002]; DUSTer [Dorr et al. 2002]
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Direct MT: Pros and Cons
Pros– Fast– Simple– Inexpensive– No translation rules hidden in lexicon
Cons– Unreliable– Not powerful– Rule proliferation– Requires too much context– Major restructuring after lexical substitution
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Transfer MT: Pros and Cons
Pros– Don’t need to find language-neutral rep– Relatively fast
Cons– N2 sets of transfer rules: Difficult to extend– Proliferation of language-specific rules in
lexicon and syntax– Cross-language generalizations lost
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Interlingual MT: Pros and Cons
Pros– Portable (avoids N2 problem)– Lexical rules and structural transformations stated more
simply on normalized representation– Explanatory Adequacy
Cons– Difficult to deal with terms on primitive level:
universals?– Must decompose and reassemble concepts– Useful information lost (paraphrase)
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Approximate IL Approach
Tap into richness of TL resources
Use some, but not all, components of IL representation
Generate multiple sentences that are statistically pared down
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Approximating IL: Handling Divergences
PrimitivesSemantic RelationsLexical Information
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Interlingual vs. Approximate IL
Interlingual MT:– primitives & relations– bi-directional lexicons– analysis: compose IL– generation: decompose IL
Approximate IL– hybrid symbolic/statistical design– overgeneration with statistical ranking– uses dependency rep input and structural expansion
for “deeper” overgeneration
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Mapping from Input Dependency to English Dependency Tree
Knowledge Resources in English only: (LVD; Dorr, 2001).
Goal
GIVEV
MARY KICKN JOHN
ThemeAgent
[CAUSE GO]
Goal
KICKV
MARY JOHN
Agent
[CAUSE GO]
Mary le dio patadas a John → Mary kicked John
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Statistical Extraction
Mary kicked John . [-0.670270 ]
Mary gave a kick at John . [-2.175831]
Mary gave the kick at John . [-3.969686]
Mary gave an kick at John . [-4.489933]
Mary gave a kick by John . [-4.803054]
Mary gave a kick to John . [-5.045810]
Mary gave a kick into John . [-5.810673]
Mary gave a kick through John . [-5.836419]
Mary gave a foot wound by John . [-6.041891]
Mary gave John a foot wound . [-6.212851]
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Benefits of Approximate IL Approach
Explaining behaviors that appear to be statistical in nature
“Re-sourceability”: Re-use of already existing components for MT from new languages.
Application to monolingual alternations
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What Resources are Required?
Deep TL resourcesRequires SL parser and tralexTL resources are richer: LVD
representations, CatVar databaseConstrained overgeneration