1 CS188 Guest Lecture: Statistical Natural Language Processing Prof. Marti Hearst School of Information Management & Systems

2 School of Information Management & Systems

3 SIMS Information economics and policy Sociology of information Human- computer interaction Information assurance Information design and architecture

4 How do we Automatically Analyze Human Language? The answer is … forget all that logic and inference stuff you’ve been learning all semester! Instead, we do something entirely different. Gather HUGE collections of text, and compute statistics over them. This allows us to make predictions. Nearly always a VERY simple algorithm and a VERY large text collection do better than a smart algorithm using knowledge engineering.

5 Statistical Natural Language Processing Chapter 23 of the textbook Prof. Russell said it won’t be on the final Today: 3 Applications Author Identification Speech Recognition (language models) Spelling Correction

6 Slide adapted from Fred S. Roberts Author Identification 1.Disputed authorship (choose among k known authors) 2.Document pair analysis: Were two documents written by the same author? 3.Odd-person-out: Were these documents written by one of this set of authors or by someone else? 4.Clustering of “putative” authors (e.g., internet handles: termin8r, heyr, KaMaKaZie) Problem Variations

7 Slide adapted form Glenn Fung The Federalist Papers  Written in by Alexander Hamilton, John Jay and James Madison to persuade the citizens of New York to ratify the constitution.  Papers consisted of short essays, 900 to 3500 words in length.  Authorship of 12 of those papers have been in dispute (Madison or Hamilton). These papers are referred to as the disputed Federalist papers.

8 Stylometry The use of metrics of literary style to analyze texts. Sentence length Paragraph length Punctuation Density of parts of speech Vocabulary Mosteller & Wallace, 1964 Federalist papers problem Used Naïve Bayes and 30 “marker” words more typical of one or the other author Concluded the disputed documents written by Madison.

9 Slide adapted from Glenn Fung  Find a hyperplane based on 3 words: to upon would=  All disputed papers end up on the Madison side of the plane. An Alternative Method (Fung)

10 Slide adapted from Glenn Fung

11 Slide adapted from Fred S. Roberts Features for Author ID Typically seek a small number of textual characteristics that distinguish the texts of authors (Burrows, Holmes, Binongo, Hoover, Mosteller & Wallace, McMenamin, Tweedie, etc.) Typically use “function words” (a, with, as, were, all, would, etc.) followed by analysis Function words are “topic-independent” However, Hoover (2003) shows that using all high- frequency words does a better job than function words alone.

12 Slide adapted from Fred S. Roberts Idiosyncratic usage (misspellings, repeated neologisms, etc.) are apparently also useful. For example, Foster’s unmasking of Klein as the author of “Primary Colors”: “Klein and Anonymous loved unusual adjectives ending in -y and –inous: cartoony, chunky, crackly, dorky, snarly,…, slimetudinous, vertiginous, …” “Both Klein and Anonymous added letters to their interjections: ahh, aww, naww.” “Both Klein and Anonymous loved to coin words beginning in hyper-, mega-, post-, quasi-, and semi- more than all others put together” “Klein and Anonymous use “riffle” to mean rifle or rustle, a usage for which the OED provides no instance in the past thousand years” Idiosyncratic Features

13 Language Modeling A fundamental concept in NLP Main idea: For a given language, some words are more likely than others to follow each other, or You can predict (with some degree of accuracy) the probability that, given a word, a particular other word will follow it.

14 Adapted from slide by Bonnie Dorr Next Word Prediction From a NY Times story... Stocks... Stocks plunged this …. Stocks plunged this morning, despite a cut in interest rates Stocks plunged this morning, despite a cut in interest rates by the Federal Reserve, as Wall... Stocks plunged this morning, despite a cut in interest rates by the Federal Reserve, as Wall Street began

15 Adapted from slide by Bonnie Dorr Stocks plunged this morning, despite a cut in interest rates by the Federal Reserve, as Wall Street began trading for the first time since last … Stocks plunged this morning, despite a cut in interest rates by the Federal Reserve, as Wall Street began trading for the first time since last Tuesday's terrorist attacks.

16 Adapted from slide by Bonnie Dorr Next Word Prediction Clearly, we have the ability to predict future words in an utterance to some degree of accuracy. How? Domain knowledge Syntactic knowledge Lexical knowledge Claim: A useful part of the knowledge needed to allow word prediction can be captured using simple statistical techniques In particular, we'll rely on the notion of the probability of a sequence (a phrase, a sentence)

17 Adapted from slide by Bonnie Dorr Applications of Language Models Why do we want to predict a word, given some preceding words? Rank the likelihood of sequences containing various alternative hypotheses, –e.g. for spoken language recognition Theatre owners say unicorn sales have doubled... Theatre owners say popcorn sales have doubled... Assess the likelihood/goodness of a sentence –for text generation or machine translation. The doctor recommended a cat scan. El doctor recommendó una exploración del gato.

18 Adapted from slide by Bonnie Dorr N-Gram Models of Language Use the previous N-1 words in a sequence to predict the next word Language Model (LM) unigrams, bigrams, trigrams,… How do we train these models? Very large corpora

19 Notation P(unicorn) Read this as “The probability of seeing the token unicorn” P(unicorn|mythical) Called the Conditional Probability. Read this as “The probability of seeing the token unicorn given that you’ve seen the token mythical

20 Adapted from slide by Bonnie Dorr Speech Recognition Example From BeRP: The Berkeley Restaurant Project (Jurafsky et al.) A testbed for a Speech Recognition project System prompts user for information in order to fill in slots in a restaurant database. –Type of food, hours open, how expensive After getting lots of input, can compute how likely it is that someone will say X given that they already said Y. P(I want to each Chinese food) = P(I | ) P(want | I) P(to | want) P(eat | to) P(Chinese | eat) P(food | Chinese)

21 Adapted from slide by Bonnie Dorr A Bigram Grammar Fragment from BeRP.001Eat British.03Eat today.007Eat dessert.04Eat Indian.01Eat tomorrow.04Eat a.02Eat Mexican.04Eat at.02Eat Chinese.05Eat dinner.02Eat in.06Eat lunch.03Eat breakfast.06Eat some.03Eat Thai.16Eat on

22 Adapted from slide by Bonnie Dorr.01British lunch.05Want a.01British cuisine.65Want to.15British restaurant.04I have.60British food.08I don’t.02To be.29I would.09To spend.32I want.14To have.02 I’m.26To eat.04 Tell.01Want Thai.06 I’d.04Want some.25 I

23 Adapted from slide by Bonnie Dorr P(I want to eat British food) = P(I| ) P(want|I) P(to|want) P(eat|to) P(British|eat) P(food|British) =.25*.32*.65*.26*.001*.60 = vs. I want to eat Chinese food = Probabilities seem to capture “syntactic'' facts, “world knowledge'' eat is often followed by an NP British food is not too popular N-gram models can be trained by counting and normalization

24 Spelling Correction How to do it? Standard approach Rely on a dictionary for comparison Assume a single “point change” –Insertion, deletion, transposition, substitution –Don’t handle word substitution Problems Might guess the wrong correction Dictionary not comprehensive –Shrek, Britney Spears, nsync, p53, ground zero May spell the word right but use it in the wrong place –principal, principle –read, red

25 New Approach: Use Search Engine Query Logs! Leverage off of the mistakes and corrections that millions of other people have already made!

26 Spelling Correction via Query Logs Cucerzan and Brill ‘04 Main idea: Iteratively transform the query into other strings that correspond to more likely queries. Use statistics from query logs to determine likelihood. –Despite the fact that many of these are misspelled –Assume that the less wrong a misspelling is, the more frequent it is, and correct > incorrect Example: ditroitigers -> detroittigers -> detroit tigers

27 Spelling Correction via Query Logs (Cucerzan and Brill ’04)

28 Spelling Correction Algorithm Algorithm: Compute the set of all possible alternatives for each word in the query –Look at word unigrams and bigrams from the logs –This handles concatenation and splitting of words Find the best possible alternative string to the input –Do this efficiently with a modified Viterbi algorithm Constraints: No 2 adjacent in-vocabulary words can change simultaneously Short queries have further (unstated) restrictions In-vocabulary words can’t be changed in the first round of iteration

29 Spelling Correction Algorithm Comparing string similarity Damerau-Levenshtein edit distance: –The minimum number of point changes required to transform a string into another Trading off distance function leniency: A rule that allows only one letter change can’t fix: –dondal duck -> donald duck A too permissive rule makes too many errors: –log wood -> dog food Actual measure: “A modified context-dependent weighted Damerau- Levenshtein edit function” –Point changes: insertion, deletion, substitution, immediate transpositions, long-distance movement of letters –“Weights interactively refined using statistics from query logs”

30 Spelling Correction Evaluation Emphasizing coverage 1044 randomly chosen queries Annotated by two people (91.3% agreement) 180 misspelled; annotators provided corrections 81.1% system agreement with annotators –131 false positives  2002 kawasaki ninja zx6e -> 2002 kawasaki ninja zx6r –156 suggestions for the misspelled queries 2 iterations were sufficient for most corrections Problem: annotators were guessing user intent

31 Spell Checking: Summary Can use the collective knowledge stored in query logs Works pretty well despite the noisiness of the data Exploits the errors made by people Might be further improved to incorporate text from other domains

32 Other Search Engine Applications Many other applications apply to search engines and related topics. One more example … automatic synonym and related word generation.

33 Synonym Generation

34 Synonym Generation

35 Synonym Generation

36 Speaking of Search Engines … Introducing a New Course! Search Engines: Technology, Society, and Business IS141 (2 units) Mondays 4-6pm + 1hr section CCN No prerequisites

37 A Great Line-up of World-Class Experts!

38 A Great Line-up of World-Class Experts!

39 Thank you! Prof. Marti Hearst School of Information Management & Systems