Textbooks you need Manning C D

*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. 2 nd edition • Jurafsky, D. and J. H. Martin: • Speech and Language Processing. Prentice-Hall. 2009. 2 nd edition 1

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 0262 -10066 -5 • Proceedings of major conferences: – – – ACL (Assoc. of Computational Linguistics) NAACL HLT (North American Chapter of ACL) COLING (Intl. Committee of Computational Linguistics) ACM SIGIR Interspeech/ASRU/SLT 2

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. 3

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, . . . ) 4

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? ) 5

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 … 6

Dan Jurafsky Question Answering: IBM’s Watson • Won Jeopardy on February 16, 2011! WILLIAM WILKINSON’S “AN ACCOUNT OF THE PRINCIPALITIES OF WALLACHIA AND MOLDOVIA” INSPIRED THIS AUTHOR’S MOST FAMOUS NOVEL 7 Bram Stoker

Dan Jurafsky Information Extraction Event: Curriculum mtg Date: Jan-16 -2012 Subject: curriculum meeting Start: 10: 00 am Date: January 15, 2012 End: 11: 30 am To: Dan Jurafsky Where: Gates 159 Hi Dan, we’ve now scheduled the curriculum meeting. It will be in Gates 159 tomorrow from 10: 00 -11: 30. -Chris Create new Calendar entry 8

Dan Jurafsky Information Extraction & Sentiment Analysis Attributes: zoom affordability size and weight flash ease of use Size and weight • 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 ✗ 9

Dan Jurafsky Machine Translation • Fully automatic • Helping human translators 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. 10

Dan Jurafsky Language Technology making good progress still really hard Sentiment analysis mostly solved Best roast chicken in San Francisco! Question answering (QA) The waiter ignored us for 20 minutes. Spam detection ✓ Let’s go to Agra! ✗ Buy V 1 AGRA … Part-of-speech (POS) tagging ADJ NOUN VERB Q. How effective is ibuprofen in reducing fever in patients with acute febrile illness? Coreference resolution ADV Colorless green ideas sleep furiously. Carter told Mubarak he shouldn’t run again. Word sense disambiguation (WSD) XYZ acquired ABC yesterday ABC has been taken over by XYZ I need new batteries for my mouse. Summarization Parsing The Dow Jones is up I can see Alcatraz from the window! Named entity recognition (NER) PERSON ORG LOC Einstein met with UN officials in Princeton Paraphrase Machine translation (MT) The S&P 500 jumped Housing prices rose Dialog 第 13届上海国际电影节开幕… Economy is good Where is Citizen Kane playing in SF? The 13 th Shanghai International Film Festival… Castro Theatre at 7: 30. Do you want a ticket? Information extraction (IE) You’re invited to our dinner party, Friday May 27 at 8: 30 Party May 27 add

Dan Jurafsky Ambiguity makes NLP hard: 100 % “Crash blossoms” REA L 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

Dan Jurafsky Ambiguity is pervasive New York Times headline (17 May 2000) Fed raises interest rates 0. 5%

Dan Jurafsky Why else is natural language understanding difficult? non-standard English segmentation issues idioms Great job @justinbieber! Were SOO PROUD of what youve accomplished! U taught us 2 #neversaynever & yourself should never give up either♥ the New York-New Haven Railroad dark horse get cold feet lose face throw in the towel neologisms unfriend Retweet bromance world knowledge Mary and Sue are sisters. Mary and Sue are mothers. But that’s what makes it fun! tricky entity names Where is A Bug’s Life playing … Let It Be was recorded … … a mutation on the for gene …

Dan Jurafsky 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.

Dan Jurafsky 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 • 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? 17

What’s hard – 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? 18

NLP: The Main Issues • Why is NLP difficult? – many “words”, many “phenomena” --> many “rules” • OED: 400 k 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 19

Difficulties in NLP (cont. ) – ambiguity • books: NOUN or VERB? – you need many books vs. 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 wind or. . . catch the hat ? 20

(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? ) 21

Solutions • 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! 22

Statistical NLP • Imagine: – Each sentence W = { w 1, w 2, . . . , 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 interpretation P(W) “ungrammatical” sentences 23

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 P(W) Wbest Wworst 24