cs188-lecture - UC Berkeley School of Information

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CS188 Guest Lecture:
Statistical Natural Language Processing
Prof. Marti Hearst
School of Information Management & Systems
www.sims.berkeley.edu/~hearst
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School of Information Management & Systems
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School of Information Management & Systems
Information
economics
and policy
Humancomputer
interaction
Information
design and
architecture
SIMS
Information
assurance
Sociology of
information
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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.
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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
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Author Identification
Problem Variations
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)
Slide adapted from Fred S. Roberts
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The Federalist Papers
 Written in 1787-1788 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.
Slide adapted form Glenn Fung
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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.
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An Alternative Method (Fung)
 Find a hyperplane based on 3 words:
0.5368 to +24.6634 upon+2.9532would=66.6159
 All disputed papers end up on the
Madison side of the plane.
Slide adapted from Glenn Fung
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Slide adapted from Glenn Fung
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Idiosyncratic Features
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 semimore 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”
Slide adapted from Fred S. Roberts
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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.
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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
Adapted from slide by Bonnie Dorr
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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.
Adapted from slide by Bonnie Dorr
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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)
Adapted from slide by Bonnie Dorr
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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.
Adapted from slide by Bonnie Dorr
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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
Adapted from slide by Bonnie Dorr
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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
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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 | <start>) P(want | I) P(to | want) P(eat | to)
P(Chinese | eat) P(food | Chinese)
Adapted from slide by Bonnie Dorr
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A Bigram Grammar Fragment from BeRP
Eat on
.16
Eat Thai
.03
Eat some
.06
Eat breakfast
.03
Eat lunch
.06
Eat in
.02
Eat dinner
.05
Eat Chinese
.02
Eat at
.04
Eat Mexican
.02
Eat a
.04
Eat tomorrow
.01
Eat Indian
.04
Eat dessert
.007
Eat today
.03
Eat British
.001
Adapted from slide by Bonnie Dorr
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<start> I
.25
Want some
.04
<start> I’d
.06
Want Thai
.01
<start> Tell
.04
To eat
.26
<start> I’m
.02
To have
.14
I want
.32
To spend
.09
I would
.29
To be
.02
I don’t
.08
British food
.60
I have
.04
British restaurant
.15
Want to
.65
British cuisine
.01
Want a
.05
British lunch
.01
Adapted from slide by Bonnie Dorr
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P(I want to eat British food) = P(I|<start>)
P(want|I) P(to|want) P(eat|to) P(British|eat)
P(food|British) = .25*.32*.65*.26*.001*.60 =
.000080
vs. I want to eat Chinese food = .00015
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
Adapted from slide by Bonnie Dorr
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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
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New Approach:
Use Search Engine Query Logs!
Leverage off of the mistakes and corrections that
millions of other people have already made!
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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
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Spelling Correction via Query Logs
(Cucerzan and Brill ’04)
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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
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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
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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
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Other Search Engine Applications
Many other applications apply to search engines and
related topics.
One more example … automatic synonym and related
word generation.
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Synonym Generation
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Synonym Generation
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Synonym Generation
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Speaking of Search Engines
… Introducing a New Course!
Search Engines: Technology, Society, and Business
IS141 (2 units)
Mondays 4-6pm + 1hr section
CCN 42702
No prerequisites
http://www.sims.berkeley.edu/courses/is141/f05/
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A Great Line-up of World-Class Experts!
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A Great Line-up of World-Class Experts!
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Thank you!
Prof. Marti Hearst
School of Information Management & Systems
www.sims.berkeley.edu/~hearst
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