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Unambiguous + Unlimited = Unsupervised
Using the Web for Natural Language Processing Problems
Marti HearstSchool of Information, UC Berkeley
UCB Neyman SeminarOctober 25, 2006
This research supported in part by NSF DBI-0317510
Marti Hearst, Neyman Seminar, 2006
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 Speech recognition is decent in limited circumstances Text categorization works with some accuracy
Marti Hearst, Neyman Seminar, 2006
How can a machine understand these differences?
Get the cat with the gloves.
Marti Hearst, Neyman Seminar, 2006
How can a machine understand these differences?
Get the sock from the cat with the gloves.
Get the glove from the cat with the socks.
Marti Hearst, Neyman Seminar, 2006
How can a machine understand these differences?
Decorate the cake with the frosting. Decorate the cake with the kids. Throw out the cake with the frosting. Throw out the cake with the kids.
Marti Hearst, Neyman Seminar, 2006
Why is this difficult?
Same syntactic structure, different meanings.
Natural language processing algorithms have to deal with the specifics of individual words.
Enormous vocabulary sizes. 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!
Marti Hearst, Neyman Seminar, 2006
How to tackle this problem?
The field was stuck for quite some time. Hand-enter all semantic concepts and relations
A new approach started around 1990 Get large text collections Compute statistics over the words in those
collections
There are many different algorithms.
Marti Hearst, Neyman Seminar, 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
Marti Hearst, Neyman Seminar, 2006
Example Problem
Grammar checker example:Which word to use? <principal> <principle>
Solution: use well-edited text and 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)
Marti Hearst, Neyman Seminar, 2006
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!
Marti Hearst, Neyman Seminar, 2006
How to Extend this Idea?
This is an exciting result … BUT relies on having huge amounts of
text that has been appropriately annotated!
Marti Hearst, Neyman Seminar, 2006
How to Avoid Manual 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(w-1, t, w+1) The largest count wins
Marti Hearst, Neyman Seminar, 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
But lacking in others spelling correction countability detection prepositional phrase attachment
How to push this idea further?
Significantly better than the best supervised algorithm.
Not significantly different from the best supervised.
Marti Hearst, Neyman Seminar, 2006
Using Unambiguous Cases
The trick: look for unambiguous cases to start
Use these to improve the results beyond what co-occurrence 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.
Question: does n2 attach to v or to n1?
Marti Hearst, Neyman Seminar, 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 with a fork. (no direct
object)
Use these to improve the results beyond what co-occurrence statistics indicate.
Marti Hearst, Neyman Seminar, 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 (with Preslav Nakov): Structural Ambiguity Decisions
PP-attachment Noun compound bracketing Coordination grouping
Semantic Relation Acquisition Hypernym (ISA) relations Verbal relations between nouns
SAT Analogy problems
Marti Hearst, Neyman Seminar, 2006
Applying U + U = U to Structural Ambiguity
We introduce the use of (nearly) unambiguous features: Surface features Paraphrases
Combined with ngrams Use from very, very large corpora Achieve state-of-the-art results without
labeled examples.
Marti Hearst, Neyman Seminar, 2006
Noun Compound Bracketing
(a) [ [ liver cell ] antibody ] (left bracketing)(b) [ liver [cell line] ] (right bracketing)
In (a), the antibody targets the liver cell.In (b), the cell line is derived from the liver.
Marti Hearst, Neyman Seminar, 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
left bracketing : [ [w1w2 ]w3] only 1 modificational choice possible
law enforcement officer
w1 w2 w3
w1 w2 w3
Marti Hearst, Neyman Seminar, 2006
Our U + U + U Algorithm
Compute bigram estimates Compute estimates from surface features Compute estimates from paraphrases Combine these scores with a voting
algorithm to choose left or right bracketing.
We use the same general approach for two other structural ambiguity problems.
Marti Hearst, Neyman Seminar, 2006
Computing Bigram Statistics
Dependency Model, FrequenciesCompare #(w1,w2) to #(w1,w3)
Dependency model, Probabilities
Pr(left) = Pr(w1w2|w2)Pr(w2w3|w3)
Pr(right) = Pr(w1w3|w3)Pr(w2w3|w3)
So we compare Pr(w1w2|w2) to Pr(w1w3|w3)
w1 w2 w3
left
right
Marti Hearst, Neyman Seminar, 2006
Using ngrams to estimate probabilities
Using page hits as a proxy for n-gram counts
Pr(w1w2|w2) = #(w1,w2) / #(w2) #(w2) word frequency; query for “w2” #(w1,w2) bigram frequency; query for “w1 w2”
smoothed by 0.5 Use 2 to determine if w1 is associated with w2
(thus indicating left bracketing), and same for w1 with w3
Marti Hearst, Neyman Seminar, 2006
Our U + U + U Algorithm
Compute bigram estimates Compute estimates from surface features Compute estimates from paraphrases Combine these scores with a voting
algorithm to choose left or right bracketing.
Marti Hearst, Neyman Seminar, 2006
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.
Marti Hearst, Neyman Seminar, 2006
Web-derived Surface Features:Dash (hyphen)
Left dash cell-cycle analysis left
Right dash donor T-cell right
Double dash T-cell-depletion unusable…
Marti Hearst, Neyman Seminar, 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
Marti Hearst, Neyman Seminar, 2006
Web-derived Surface Features:Capitalization
anycase – lowercase – uppercase Plasmodium vivax Malaria left plasmodium vivax Malaria left
lowercase – uppercase – anycase brain Stem cell right brain Stem Cell right
Disable this on: Roman digits Single-letter words: e.g. vitamin D
deficiency
Marti Hearst, Neyman Seminar, 2006
Web-derived Surface Features:Embedded Slash
Left embedded slash leukemia/lymphoma cell right
Marti Hearst, Neyman Seminar, 2006
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
Marti Hearst, Neyman Seminar, 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
Marti Hearst, Neyman Seminar, 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
Marti Hearst, Neyman Seminar, 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
Marti Hearst, Neyman Seminar, 2006
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”
Marti Hearst, Neyman Seminar, 2006
Other Web-derived Features:Reorder
Reorders for “health care reform” “care reform health” right “reform health care” left
Marti Hearst, Neyman Seminar, 2006
Other Web-derived Features:Internal Inflection Variability
Vary inflection of second word tyrosine kinase activation tyrosine kinases activation
Marti Hearst, Neyman Seminar, 2006
Other Web-derived Features:Switch The First Two Words
Predict right, if we can reorder adult male rat as male adult rat
Marti Hearst, Neyman Seminar, 2006
Our U + U + U Algorithm
Compute bigram estimates Compute estimates from surface features Compute estimates from paraphrases Combine these scores with a voting
algorithm to choose left or right bracketing.
Marti Hearst, Neyman Seminar, 2006
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
Marti Hearst, Neyman Seminar, 2006
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.
Marti Hearst, Neyman Seminar, 2006
Our U + U + U Algorithm
Compute bigram estimates Compute estimates from surface features Compute estimates from paraphrases Combine these scores with a voting
algorithm to choose left or right bracketing.
Marti Hearst, Neyman Seminar, 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)
Marti Hearst, Neyman Seminar, 2006
Paraphrase and Surface Features Performance
Lauer Set
Biomedical Set
Marti Hearst, Neyman Seminar, 2006
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)
Marti Hearst, Neyman Seminar, 2006
Prepositional Phrase Attachment
Problem: (a) Peter spent millions of dollars. (noun
attach)
(b) Peter spent time with his family. (verb attach)
Which attachment for quadruple: (v, n1, p, n2)
Results:Much simpler than other algorithmsAs good as or better than best unsupervised, and better than some supervised approaches
Marti Hearst, Neyman Seminar, 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.
Marti Hearst, Neyman Seminar, 2006
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
Marti Hearst, Neyman Seminar, 2006
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
Marti Hearst, Neyman Seminar, 2006
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.
Marti Hearst, Neyman Seminar, 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.
Marti Hearst, Neyman Seminar, 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 …
Marti Hearst, Neyman Seminar, 2006
Uncovering 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
Marti Hearst, Neyman Seminar, 2006
Tackling the SAT Analogy Problem
First issue queries to find the relations (features) that hold between each word pair
Compare the features for each answer pair to those of the question pair. Weight the features with term count and
document counts Compare the weighted feature sets using Dice
coefficient
Marti Hearst, Neyman Seminar, 2006
Extract Features from Retrieved Text
Verb The committee includes many members. This is a committee, which includes many
members. This is a committee, including many members.
Verb+Preposition The committee consists of many members.
Preposition He is a member of the committee.
Coordinating Conjunction the committee and its members
Marti Hearst, Neyman Seminar, 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
To counter the labeled-data roadblock, we start with unambiguous features that we can find naturally. We’ve applied this to structural and semantic language
problems. These are stepping stones towards sophisticated language
understanding.
Marti Hearst, Neyman Seminar, 2006
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”
Marti Hearst, Neyman Seminar, 2006
Using n-grams to make predictions
Use search engines page hits as a proxy for n-gram counts compare Pr(w1w2|w2) to Pr(w1w3|w3)
Pr(w1 w2|w2 ) = #(w1,w2) / #(w2) #(w2) word frequency; query for “w2”
#(w1,w2) bigram frequency; query for “w1 w2”
Marti Hearst, Neyman Seminar, 2006
Probabilities: Why? (1)
Why should we use: (a) Pr(w1w2|w2), rather than (b) Pr(w2w1|w1)?
Keller&Lapata (2004) calculate: AltaVista queries:
(a): 70.49% (b): 68.85%
British National Corpus: (a): 63.11% (b): 65.57%
Marti Hearst, Neyman Seminar, 2006
Probabilities: Why? (2)
Why should we use: (a) Pr(w1w2|w2), rather than
(b) Pr(w2w1|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)
Marti Hearst, Neyman Seminar, 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)
dependency model Does w1 modify w3?
(vs. w1 modifying w2)
w1 w2 w3
w1 w2 w3
w1 w2 w3
Marti Hearst, Neyman Seminar, 2006
Paraphrases: pattern (1)
(1)v n1 p n2 v n2 n1 (noun)
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 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
Marti Hearst, Neyman Seminar, 2006
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.
Marti Hearst, Neyman Seminar, 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
Marti Hearst, Neyman Seminar, 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
Marti Hearst, Neyman Seminar, 2006
Paraphrases: pattern (5)
(5)v n1 p n2 v PRONOUN p n2 (verb)
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
pronoun