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*Textbooks you need. Manning, C. D., Sch ü tze, H.: Foundations of Statistical Natural Language Processing . The MIT Press. 1999. ISBN 0-262-13360-1. [required] Allen, J.: Natural Language Understanding . The Benjamins/Cummins Publishing Co. 1995. 2 nd edition

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textbooks you need
*Textbooks you need
  • Manning, C. D., Schütze, H.:
      • Foundations of Statistical Natural Language Processing. The MIT Press. 1999. ISBN 0-262-13360-1.[required]
  • Allen, J.:
      • Natural Language Understanding. The Benjamins/Cummins Publishing Co. 1995. 2nd edition
  • Jurafsky, D. and J. H. Martin:
      • Speech and Language Processing. Prentice-Hall. 2009. 2nd edition
other reading
Other reading
  • Charniak, E:
    • Statistical Language Learning. The MIT Press. 1996. ISBN 0-262-53141-0.
  • Cover, T. M., Thomas, J. A.:
    • Elements of Information Theory. Wiley. 1991. ISBN 0-471-06259-6.
  • Jelinek, F.:
    • Statistical Methods for Speech Recognition. The MIT Press. 1998. ISBN 0-262-10066-5
  • Proceedings of major conferences:
    • ACL (Assoc. of Computational Linguistics)
    • NAACL HLT (North American Chapter of ACL)
    • COLING (Intl. Committee of Computational Linguistics)
    • Interspeech/ASRU/SLT
course segments
Course segments
  • Intro & Probability & Information Theory
    • The very basics: definitions, formulas, examples.
  • Language Modeling
    • n-gram models, parameter estimation
    • smoothing (EM algorithm)
  • A Bit of Linguistics
    • phonology, morphology, syntax, semantics, discourse
  • Words and the Lexicon
    • word classes, mutual information, bit of lexicography.
course segments cont
Course segments (cont.)
  • Hidden Markov Models
    • background, algorithms, parameter estimation
  • Tagging: Methods, Algorithms, Evaluation
    • tagsets, morphology, lemmatization
    • HMM tagging, Transformation-based, Feature-based
  • NL Grammars and Parsing: Data, Algorithms
    • Grammars and Automata, Deterministic Parsing
    • Statistical parsing. Algorithms, parameterization, evaluation
  • Applications (MT, ASR, IR, Q&A, ...)
goals of the hlt
Goals of the HLT

Computers would be a lot more useful if they could handle our email, do our library research, talk to us …

But they are fazed by natural human language.

How can we make computers have abilities to handle human language? (Or help them learn it as kids do?)

a few applications of hlt
A few applications of HLT
  • Spelling correction, grammar checking …(language learning and evaluation e.g. TOEFL essay score)
  • Better search engines
  • Information extraction, gisting
  • Psychotherapy; Harlequin romances; etc.
  • New interfaces:
    • Speech recognition (and text-to-speech)
    • Dialogue systems (USS Enterprise onboard computer)
    • Machine translation; speech translation (the Babel tower??)
  • Trans-lingual summarization, detection, extraction …
question answering ibm s watson
Question Answering: IBM’s Watson
  • Won Jeopardy on February 16, 2011!





Bram Stoker

information extraction
Information Extraction

Event: Curriculum mtg

Date: Jan-16-2012

Start: 10:00am

End: 11:30am

Where: Gates 159

Subject: curriculum meeting

Date: January 15, 2012

To: Dan Jurafsky

Hi Dan, we’ve now scheduled the curriculum meeting.

It will be in Gates 159 tomorrow from 10:00-11:30.


Create new Calendar entry

information extraction sentiment analysis
Information Extraction & Sentiment Analysis




size and weight


ease of use

  • nice and compact to carry!
  • since the camera is small and light, I won't need to carry around those heavy, bulky professional cameras either!
  • the camera feels flimsy, is plastic and very light in weight you have to be very delicate in the handling of this camera

Size and weight

machine translation
Machine Translation
  • Helping human translators
  • Fully automatic

Enter Source Text:

 这 不过 是 一 个 时间 的 问题 .

Translation from Stanford’s Phrasal:

This is only a matter of time.

language technology
Language Technology

making good progress

Sentiment analysis

still really hard

mostly solved

Best roast chicken in San Francisco!

Question answering (QA)

The waiter ignored us for 20 minutes.

Q. How effective is ibuprofen in reducing fever in patients with acute febrile illness?

Coreference resolution

Spam detection

Let’s go to Agra!


Carter told Mubarak he shouldn’t run again.

Buy V1AGRA …

Word sense disambiguation (WSD)

XYZ acquired ABC yesterday

ABC has been taken over by XYZ

Part-of-speech (POS) tagging

I need new batteries for my mouse.




Colorless green ideas sleep furiously.

The Dow Jones is up

Economy is good

The S&P500 jumped

I can see Alcatraz from the window!

Named entity recognition (NER)

Housing prices rose

Machine translation (MT)




Where is Citizen Kane playing in SF?

Einstein met with UN officials in Princeton

The 13th Shanghai International Film Festival…

Castro Theatre at 7:30. Do you want a ticket?

Information extraction (IE)

PartyMay 27add

You’re invited to our dinner party, Friday May 27 at 8:30

ambiguity makes nlp hard crash blossoms
Ambiguity makes NLP hard:“Crash blossoms”

Violinist Linked to JAL Crash Blossoms

Teacher Strikes Idle Kids

Red Tape Holds Up New Bridges

Hospitals Are Sued by 7 Foot Doctors

Juvenile Court to Try Shooting Defendant

Local High School Dropouts Cut in Half


ambiguity is pervasive
Ambiguity is pervasive
  • New York Times headline (17 May 2000)

Fed raises interest rates

Fed raises interest rates

Fed raises interest rates 0.5%

why else is natural language understanding difficult
Why else is natural language understanding difficult?

segmentation issues

non-standard English


Great job @justinbieber! Were SOO PROUD of what youve accomplished! U taught us 2 #neversaynever & you yourself should never give up either♥

dark horse

get cold feet

lose face

throw in the towel

the New York-New Haven Railroad

the New York-New Haven Railroad

tricky entity names

world knowledge





Where is A Bug’s Life playing …

Let It Be was recorded …

… a mutation on the for gene …

Mary and Sue are sisters.

Mary and Sue are mothers.

But that’s what makes it fun!

making progress on this problem
Making progress on this problem…
  • The task is difficult! What tools do we need?
    • Knowledge about language
    • Knowledge about the world
    • A way to combine knowledge sources
  • How we generally do this:
    • probabilistic models built from language data
      • P(“maison”  “house”) high
      • P(“L’avocat général”  “the general avocado”) low
    • Luckily, rough text features can often do half the job.
this class
This class
  • Teaches key theory and methods for statistical NLP:
    • Viterbi
    • Naïve Bayes, Maxent classifiers
    • N-gram language modeling
    • Statistical Parsing
    • Inverted index, tf-idf, vector models of meaning
  • For practical, robust real-world applications
    • Information extraction
    • Spelling correction
    • Information retrieval
    • Sentiment analysis
levels of language
Levels of Language
  • Phonetics/phonology/morphology: what words (or subwords) are we dealing with?
  • Syntax: What phrases are we dealing with? Which words modify one another?
  • Semantics: What’s the literal meaning?
  • Pragmatics: What should you conclude from the fact that I said something? How should you react?
what s hard ambiguities ambiguities all different levels of ambiguities
What’s hard – ambiguities, ambiguities, all different levels of ambiguities

John stopped at the donut store on his way home from work. He thought a coffee was good every few hours. But it turned out to be too expensive there. [from J. Eisner]

- donut: To get a donut (doughnut; spare tire) for his car?

- Donut store: store where donuts shop? or is run by donuts? or looks like a big donut? or made of donut?

- From work: Well, actually, he stopped there from hunger and exhaustion, not just from work.

- Every few hours: That’s how often he thought it? Or that’s for coffee?

- it: the particular coffee that was good every few hours? the donut store? the situation

- Too expensive: too expensive for what? what are we supposed to conclude about what John did?

nlp the main issues
NLP: The Main Issues
  • Why is NLP difficult?
    • many “words”, many “phenomena” --> many “rules”
      • OED: 400k words; Finnish lexicon (of forms): ~2 . 107
      • sentences, clauses, phrases, constituents, coordination, negation, imperatives/questions, inflections, parts of speech, pronunciation, topic/focus, and much more!
      • irregularity (exceptions, exceptions to the exceptions, ...)
      • potato -> potato es (tomato, hero,...); photo -> photo s, and even: both mango -> mango s or -> mango es
      • Adjective / Noun order: new book, electrical engineering, general regulations, flower garden, garden flower, ...: but Governor General
difficulties in nlp cont
Difficulties in NLP (cont.)
  • ambiguity
    • books: NOUN or VERB?
      • you need many booksvs. she books her flights online
    • No left turn weekdays 4-6 pm / except transit vehicles (Charles Street at Cold Spring)
      • when may transit vehicles turn: Always? Never?
    • Thank you for not smoking, drinking, eating or playing radios without earphones. (MTA bus)
      • Thank you for not eating without earphones??
      • or even: Thank you for not drinking without earphones!?
    • My neighbor’s hat was taken by wind. He tried to catch it.
      • ...catch the windor ...catch the hat ?
categorical rules or statistics
(Categorical) Rules or Statistics?
  • Preferences:
    • clear cases: context clues: she books --> books is a verb
        • rule: if an ambiguous word (verb/nonverb) is preceded by a matching personal pronoun -> word is a verb
    • less clear cases: pronoun reference
        • she/he/it refers to the most recent noun or pronoun (?) (but maybe we can specify exceptions)
    • selectional:
        • catching hat >> catching wind (but why not?)
    • semantic:
        • never thank for drinking in a bus! (but what about the earphones?)
  • Don’t guess if you know:
      • morphology (inflections)
      • lexicons (lists of words)
      • unambiguous names
      • perhaps some (really) fixed phrases
      • syntactic rules?
  • Use statistics (based on real-world data) for preferences (only?)
      • No doubt: but this is the big question!
statistical nlp
Statistical NLP
  • Imagine:
    • Each sentence W = { w1, w2, ..., wn } gets a probability P(W|X) in a context X (think of it in the intuitive sense for now)
    • For every possible context X, sort all the imaginable sentences W according to P(W|X):
    • Ideal situation:

best sentence (most probable in context X) NB: same for


P(W)“ungrammatical” sentences

real world situation
Real World Situation
  • Unable to specify set of grammatical sentences today using fixed “categorical” rules (maybe never)
  • Use statistical “model” based on REAL WORLD DATA and care about the best sentence only (disregarding the “grammaticality” issue)

best sentence


Wbest Wworst