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Unambiguous + Unlimited = Unsupervised
or
Using the Web for
Natural Language Processing Problems
Marti Hearst
School of Information, UC Berkeley
This research supported in part by NSF DBI-0317510
Natural Language Processing
 The ultimate goal: write programs that read and
understand stories and conversations.
 This is too hard! Instead we tackle sub-problems.
 There have been notable successes lately:
 Machine translation is vastly improved
 Decent speech recognition in limited circumstances
 Text categorization works with some accuracy
PARC, Aug 3, 2006
Automatic Help Desk Translation at MS
PARC, Aug 3, 2006
Why is text analysis difficult?
 One reason: enormous vocabulary size.
 The average English speaker’s vocabulary is
around 50,000 words,
 Many of these can be combined with many
others,
 And they mean different things when they do!
PARC, Aug 3, 2006
How can a machine understand these?
 Decorate the cake with the frosting.
 Decorate the cake with the kids.
 Throw out the cake with the frosting.
 Get the sock from the cat with the gloves.
 Get the glove from the cat with the socks.
 It’s in the plastic water bottle.
 It’s in the plastic bag dispenser.
PARC, Aug 3, 2006
How to tackle this problem?
 The field was stuck for quite some time.
 CYC: hand-enter all semantic concepts and relations
 A new approach started around 1990
 How to do it:
 Get large text collections
 Compute statistics over the words in those collections
 Many different algorithms for doing this.
PARC, Aug 3, 2006
Size Matters
 Recent realization: bigger is better than smarter!
 Banko and Brill ’01: “Scaling to Very, Very Large
Corpora for Natural Language Disambiguation”, ACL
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Example Problem
 Grammar checker example:
Which word to use?
<principal> <principle>
 Solution: look at which words surround each use:
 I am in my third year as the principal of Anamosa High
School.
 School-principal transfers caused some upset.
 This is a simple formulation of the quantum mechanical
uncertainty principle.
 Power without principle is barren, but principle without
power is futile. (Tony Blair)
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Using Very, Very Large Corpora
 Keep track of which words are the neighbors of each
spelling in well-edited text, e.g.:
 Principal: “high school”
 Principle: “rule”
 At grammar-check time, choose the spelling best
predicted by the surrounding words.
 Surprising results:
 Log-linear improvement even to a billion words!
 Getting more data is better than fine-tuning algorithms!
PARC, Aug 3, 2006
The Effects of LARGE Datasets
 From Banko & Brill ‘01
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How to Extend this Idea?
 This is an exciting result …
 BUT relies on having huge amounts of text
that has been appropriately annotated!
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How to Avoid Labeling?
 “Web as a baseline” (Lapata & Keller 04,05)
 Main idea: apply web-determined counts to
every problem imaginable.
 Example: for t in {<principal> <principle>}
 Compute f(w1, t, w2)
 The largest count wins
PARC, Aug 3, 2006
Web as a Baseline
 Works very well in some cases





machine translation candidate selection
article generation
noun compound interpretation
noun compound bracketing
adjective ordering
Significantly better than the
best supervised algorithm.
Not significantly different
from the best supervised.
 But lacking in others
 spelling correction
 countability detection
 prepositional phrase attachment
 How to push this idea further?
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Using Unambiguous Cases
 The trick: look for unambiguous cases to start
 Use these to improve the results beyond what cooccurrence statistics indicate.
 An Early Example:
 Hindle and Rooth, “Structural Ambiguity and Lexical
Relations”, ACL ’90, Comp Ling’93
 Problem: Prepositional Phrase attachment




I eat/v spaghetti/n1 with/p a fork/n2.
I eat/v spaghetti/n1 with/p sauce/n2.
quadruple: (v, n1, p, n2)
Question: does n2 attach to v or to n1?
PARC, Aug 3, 2006
Using Unambiguous Cases
 How to do this with unlabeled data?
 First try:
 Parse some text into phrase structure
 Then compute certain co-occurrences
f(v, n1, p) f(n1, p)
f(v, n1)
 Problem: results not accurate enough
 The trick: look for unambiguous cases:
 Spaghetti with sauce is delicious. (pre-verbal)
 I eat it with a fork. (object of preposition can’t attach
to a pronoun)
 Use these to improve the results beyond what cooccurrence statistics indicate.
PARC, Aug 3, 2006
Unambiguous + Unlimited = Unsupervised
 Apply the Unambiguous Case Idea to the Very, Very Large
Corpora idea
 The potential of these approaches are not fully realized
 Our work:
 Structural Ambiguity Decisions (work with Preslav Nakov)



PP-attachment
Noun compound bracketing
Coordination grouping
 Semantic Relation Acquisition


Hypernym (ISA) relations
Verbal relations between nouns
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Structural Ambiguity Problems
 Apply the U + U = U idea to structural ambiguity
 Noun compound bracketing
 Prepositional Phrase attachment
 Noun Phrase coordination
 Motivation: BioText project

In eukaryotes, the key to transcriptional regulation of the Heat Shock
Response is the Heat Shock Transcription Factor (HSF).

Open-labeled long-term study of the subcutaneous sumatriptan efficacy
and tolerability in acute migraine treatment.
•
BimL protein interact with Bcl-2 or Bcl-XL, or Bcl-w proteins (Immunoprecipitation (anti-Bcl-2 OR Bcl-XL or Bcl-w)) followed by Western blot
(anti-EEtag) using extracts human 293T cells co-transfected with EEtagged BimL and (bcl-2 or bcl-XL or bcl-w) plasmids)
PARC, Aug 3, 2006
Applying U + U = U to Structural Ambiguity
 We introduce the use of (nearly) unambiguous
features:
 surface features
 paraphrases
 Combined with very, very large corpora
 Achieve state-of-the-art results without labeled
examples.
PARC, Aug 3, 2006
Noun Compound Bracketing
(a)
(b)
[ [ liver cell ] antibody ]
[ liver [cell line] ]
(left bracketing)
(right bracketing)
In (a), the antibody targets the liver cell.
In (b), the cell line is derived from the liver.
PARC, Aug 3, 2006
Dependency Model
 right bracketing: [w1[w2w3] ]
 w2w3 is a compound (modified by w1)
 home health care
 w1 and w2 independently modify w3
 adult male rat
w1
w2
w3
w1
w2
w3
 left bracketing : [ [w1w2 ]w3]
 only 1 modificational choice possible
 law enforcement officer
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Related Work
 Marcus(1980), Pustejosky&al.(1993), Resnik(1993)
 adjacency model:
 Lauer (1995)
Pr(w1|w2) vs. Pr(w2|w3)
 dependency model: Pr(w1|w2) vs. Pr(w1|w3)
 Keller & Lapata (2004):
 use the Web
 unigrams and bigrams
Our approach:
 Girju & al. (2005)
• Web as data
 supervised model
2 , n-grams
•

 bracketing in context
• paraphrases
 requires WordNet senses
• surface features
to be given
PARC, Aug 3, 2006
Computing Bigram Statistics
 Dependency Model, Frequencies
Compare #(w1,w2) to #(w1,w3)
 Dependency model, Probabilities
Pr(left) = Pr(w1w2|w2)Pr(w2w3|w3)
Pr(right) = Pr(w1w3|w3)Pr(w2w3|w3)
right
w1
w2
left
 So we compare Pr(w1w2|w2) to Pr(w1w3|w3)
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w3
Probabilities: Estimation
 Using page hits as a proxy for n-gram counts
 Pr(w1w2|w2) = #(w1,w2) / #(w2)
 #(w2)
 #(w1,w2)
word frequency; query for “w2”
bigram frequency; query for “w1 w2”
 smoothed by 0.5
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Association Models: 2 (Chi Squared)





A = #(wi,wj)
B = #(wi) – #(wi,wj)
C = #(wj) – #(wi,wj)
D = N – (A+B+C)
N = 8 trillion (= A+B+C+D)
8 billion Web pages x 1,000 words
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Web-derived Surface Features
 Authors often disambiguate noun compounds using
surface markers, e.g.:
 amino-acid sequence  left
 brain stem’s cell  left
 brain’s stem cell  right
 The enormous size of the Web makes these
frequent enough to be useful.
PARC, Aug 3, 2006
Web-derived Surface Features:
Dash (hyphen)
 Left dash
 cell-cycle analysis  left
 Right dash
 donor T-cell  right
 fiber optics-system  should be left..
 Double dash
 T-cell-depletion  unusable…
PARC, Aug 3, 2006
Web-derived Surface Features:
Possessive Marker
 Attached to the first word
 brain’s stem cell  right
 Attached to the second word
 brain stem’s cell  left
 Combined features
 brain’s stem-cell  right
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Web-derived Surface Features:
Capitalization
 don’t-care – lowercase – uppercase
 Plasmodium vivax Malaria  left
 plasmodium vivax Malaria  left
 lowercase – uppercase – don’t-care
 brain Stem cell  right
 brain Stem Cell  right
 Disable this on:
 Roman digits
 Single-letter words: e.g. vitamin D deficiency
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Web-derived Surface Features:
Embedded Slash
 Left embedded slash
 leukemia/lymphoma cell  right
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Web-derived Surface Features:
Parentheses
 Single-word
 growth factor (beta)  left
 (brain) stem cell  right
 Two-word
 (growth factor) beta  left
 brain (stem cell)  right
PARC, Aug 3, 2006
Web-derived Surface Features:
Comma, dot, semi-colon
 Following the first word
 home. health care  right
 adult, male rat  right
 Following the second word
 health care, provider  left
 lung cancer: patients  left
PARC, Aug 3, 2006
Web-derived Surface Features:
Dash to External Word
 External word to the left
 mouse-brain stem cell  right
 External word to the right
 tumor necrosis factor-alpha  left
PARC, Aug 3, 2006
Web-derived Surface Features:
Problems & Solutions
 Problem: search engines ignore punctuation in
queries
 “brain-stem cell” does not work
 Solution:
 query for “brain stem cell”
 obtain 1,000 document summaries
 scan for the features in these summaries
PARC, Aug 3, 2006
Other Web-derived Features:
Abbreviation
 After the second word
 tumor necrosis factor (NF)  right
 After the third word
 tumor necrosis (TN) factor  right
 We query for, e.g., “tumor necrosis tn factor”
 Problems:
 Roman digits: IV, VI
 States: CA
 Short words: me
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Other Web-derived Features:
Concatenation
 Consider health care reform
 healthcare : 79,500,000
 carereform : 269
 healthreform: 812
 Adjacency model
 healthcare vs. carereform
 Dependency model
 healthcare vs. healthreform
 Triples
 “healthcare reform” vs. “health carereform”
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Other Web-derived Features:
Reorder
 Reorders for “health care reform”
 “care reform health”  right
 “reform health care”  left
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Other Web-derived Features:
Internal Inflection Variability
 Vary inflection of second word
 tyrosine kinase activation
 tyrosine kinases activation
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Other Web-derived Features:
Switch The First Two Words
 Predict right, if we can reorder
 adult male rat
 male adult rat
as
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Paraphrases
 The semantics of a noun compound is often made
overt by a paraphrase (Warren,1978)
 Prepositional
 stem cells in the brain  right
 cells from the brain stem  right
 Verbal
 virus causing human immunodeficiency  left
 Copula
 office building that is a skyscraper  right
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Paraphrases
 prepositional paraphrases:
 We use: ~150 prepositions
 verbal paraphrases:
 We use: associated with, caused by, contained in, derived
from, focusing on, found in, involved in, located at/in,
made of, performed by, preventing, related to and used
by/in/for.
 copula paraphrases:
 We use: is/was and that/which/who
 optional elements:
 articles: a, an, the
 quantifiers: some, every, etc.
 pronouns: this, these, etc.
PARC, Aug 3, 2006
Evaluation: Datasets
 Lauer Set
 244 noun compounds (NCs)
 from Grolier’s encyclopedia
 inter-annotator agreement: 81.5%
 Biomedical Set
 430 NCs
 from MEDLINE
 inter-annotator agreement: 88% ( =.606)
PARC, Aug 3, 2006
Evaluation: Experiments
 Exact phrase queries
 Limited to English
 Inflections:
 Lauer Set: Carroll’s morphological tools
 Biomedical Set: UMLS Specialist Lexicon
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Co-occurrence Statistics
 Lauer set
 Bio set
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Paraphrase and Surface Features Performance
 Lauer Set
 Biomedical Set
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Individual Surface Features Performance: Bio
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Individual Surface Features Performance: Bio
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Results Lauer
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Results: Comparing with Others
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Results Bio
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Summary:
Results for Noun Compound Bracketing
 Introduced search engine statistics that go
beyond the n-gram (applicable to other
tasks)
 surface features
 paraphrases
 Obtained new state-of-the-art results on NC
bracketing
 more robust than Lauer (1995)
 more accurate than Keller&Lapata (2004)
PARC, Aug 3, 2006
Prepositional Phrase Attachment
(a) Peter spent millions of dollars.
(b) Peter spent time with his family.
(noun attach)
(verb attach)
quadruple: (v, n1, p, n2)
(a) (spent, millions, of, dollars)
(b) (spent, time, with, family)
PARC, Aug 3, 2006
Noun Phrase Coordination
 (Modified) real sentence:
 The Department of Chronic Diseases and Health
Promotion leads and strengthens global efforts to
prevent and control chronic diseases or disabilities
and to promote health and quality of life.
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NC coordination: ellipsis
 Ellipsis
 car and truck production
 means car production and truck production
 No ellipsis
 president and chief executive
 All-way coordination
 Securities and Exchange Commission
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Results
428 examples from Penn TB
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Semantic Relation Detection
 Goal: automatically augment a lexical database
 Many potential relation types:
 ISA (hypernymy/hyponymy)
 Part-Of (meronymy)
 Idea: find unambiguous contexts which (nearly)
always indicate the relation of interest
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Lexico-Syntactic Patterns
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Lexico-Syntactic Patterns
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Adding a New Relation
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Semantic Relation Detection
 Lexico-syntactic Patterns:
 Should occur frequently in text
 Should (nearly) always suggest the relation of interest
 Should be recognizable with little pre-encoded
knowledge.
 These patterns have been used extensively by
other researchers.
PARC, Aug 3, 2006
Semantic Relation Detection
 What relationship holds between two nouns?
 olive oil – oil comes from olives
 machine oil – oil used on machines
 Assigning the meaning relations between these
terms has been seen as a very difficult solution
 Our solution:
 Use clever queries against the web to figure out the
relations.
PARC, Aug 3, 2006
Queries for Semantic Relations
 Convert the noun-noun compound into a query of the form:

noun2 that * noun1

“oil that * olive(s)”
 This returns search result snippets containing interesting
verbs.
 In this case:





Come from
Be obtained from
Be extracted from
Made from
…
PARC, Aug 3, 2006
Queries for Semantic Relations
 More examples:
 Migraine drug -> treat, be used for, reduce, prevent
 Wrinkle drug -> treat, be used for, reduce, smooth
 Printer tray -> hold, come with, be folded, fit under,
be inserted into
 Student protest -> be led by, be sponsored by, pit, be,
be organized by
PARC, Aug 3, 2006
Conclusions
 The enormous size of the web opens new
opportunities for text analysis
 There are many words, but they are more likely to
appear together in a huge dataset
 This allows us to do word-specific analysis
 Unambiguous + Unlimited = Unsupervised
 We’ve applied it to structural and semantic language
problems.
 These are stepping stones towards sophisticated
language understanding.
PARC, Aug 3, 2006
Thank you!
http://biotext.berkeley.edu
Supported in part by NSF DBI-0317510
Using n-grams to make predictions
 Say trying to distinguish:
[home health] care
home [health care]
 Main idea: compare these co-occurrence
probabilities
 “home health” vs
 “health care”
PARC, Aug 3, 2006
Using n-grams to make predictions
 Use search engines page hits as a proxy for n-gram
counts
 compare Pr(w1w2|w2) to Pr(w1w3|w3)
 Pr(w1 w2|w2 ) = #(w1,w2) / #(w2)
 #(w2)
 #(w1,w2)
word frequency; query for “w2”
bigram frequency; query for “w1 w2”
PARC, Aug 3, 2006
Probabilities: Why? (1)
 Why should we use:
 (a) Pr(w1w2|w2), rather than
 (b) Pr(w2w1|w1)?
 Keller&Lapata (2004) calculate:
 AltaVista queries:
 (a): 70.49%
 (b): 68.85%
 British National Corpus:
 (a): 63.11%
 (b): 65.57%
PARC, Aug 3, 2006
Probabilities: Why? (2)
 Why should we use:
 (a) Pr(w1w2|w2), rather than
 (b) Pr(w2w1|w1)?
 Maybe to introduce a bracketing prior.
 Just like Lauer (1995) did.
 But otherwise, no reason to prefer either one.
 Do we need probabilities? (association is OK)
 Do we need a directed model? (symmetry is OK)
PARC, Aug 3, 2006
Adjacency & Dependency (2)
 right bracketing: [w1[w2w3] ]
 w2w3 is a compound (modified by w1)
 w1 and w2 independently modify w3
 adjacency model
 Is w2w3 a compound?
 (vs. w1w2 being a compound)
w1
w2
w3
w1
w2
w3
w1
w2
w3
 dependency model
 Does w1 modify w3?
 (vs. w1 modifying w2)
PARC, Aug 3, 2006
Paraphrases: pattern (1)
(1)v n1 p n2  v n2 n1

Can we turn “n1 p n2” into a noun compound “n2 n1”?



meet/v demands/n1 from/p customers/n2 
meet/v the customer/n2 demands/n1
Problem: ditransitive verbs like give



(noun)
gave/v an apple/n1 to/p him/n2 
gave/v him/n2 an apple/n1
Solution:



no determiner before n1
determiner before n2 is required
the preposition cannot be to
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Paraphrases: pattern (2)
(2)v n1 p n2  v p n2 n1

(verb)
If “p n2” is an indirect object of v, then it could
be switched with the direct object n1.


had/v a program/n1 in/p place/n2 
had/v in/p place/n2 a program/n1
Determiner before n1 is required to prevent
“n2 n1” from forming a noun compound.
PARC, Aug 3, 2006
Paraphrases: pattern (3)
(3)v n1 p n2  p n2 * v n1
(verb)

“*” indicates a wildcard position (up to
three intervening words are allowed)

Looks for appositions, where the PP has
moved in front of the verb, e.g.


I gave/v an apple/n1 to/p him/n2 
to/p him/n2 I gave/v an apple/n1
PARC, Aug 3, 2006
Paraphrases: pattern (4)
(4)v n1 p n2  n1 p n2 v

(noun)
Looks for appositions, where “n1 p n2” has
moved in front of v


shaken/v confidence/n1 in/p markets/n2 
confidence/n1 in/p markets/n2 shaken/v
PARC, Aug 3, 2006
Paraphrases: pattern (5)
(5)v n1 p n2  v PRONOUN p n2
(verb)
pronoun
 n1 is a pronoun  verb (Hindle&Rooth, 93)

Pattern (5) substitutes n1 with a dative pronoun
(him or her), e.g.


put/v a client/n1 at/p odds/n2 
put/v him at/p odds/n2
PARC, Aug 3, 2006
Paraphrases: pattern (6)
(6)v n1 p n2  BE n1 p n2
(noun)
to
be
 BE is typically used with a noun attachment

Pattern (6) substitutes v with a form of to be (is
or are), e.g.


eat/v spaghetti/n1 with/p sauce/n2 
is spaghetti/n1 with/p sauce/n2
PARC, Aug 3, 2006