Using the Web as an Implicit Training Set:

Application to Structural Ambiguity Resolution

Using the Web as an Implicit Training Set:

Application to Structural Ambiguity Resolution

Preslav Nakov and Marti HearstComputer Science Division and SIMS

University of California, Berkeley

Supported by NSF DBI-0317510 and a gift from Genentech

Motivation

Huge datasets trump sophisticated algorithms. “Scaling to Very Very Large Corpora for Natural

Language Disambiguation”, ACL 2001 (Banko & Brill, 2001)

Task: spelling correction Raw text as “training data” Log-linear improvement even to billion words

Getting more data is better than fine-tuning algorithms.

How to generalize to other problems?

Web as a Baseline

“Web as a baseline” (Lapata & Keller 04;05): applied simple n-gram models to: machine translation candidate selection article generation noun compound interpretation noun compound bracketing adjective ordering spelling correction countability detection prepositional phrase attachment

All unsupervised Findings:

Sometimes rival best supervised approaches. => Web n-grams should be used as a baseline.

Significantly better than the best supervised algorithm.

Not significantly different from the best supervised algorithm.

Our Contribution

Potential of these ideas is not yet fully realized We introduce new features

paraphrases surface features

Applied to structural ambiguity problems Data sparseness: need statistics for every possible

word and for word combinations Problems (unsupervised):

Noun compound bracketing PP attachment NP coordination

state-of-the-art results(Nakov&Hearst, 2005)

this work

Task 1:

Prepositional Phrase

Attachment

PP attachment

(a) Peter spent millions of dollars. (noun)

(b) Peter spent time with his family. (verb)

quadruple: (v, n1, p, n2)

(a) (spent, millions, of, dollars)

(b) (spent, time, with, family)

Human performance: quadruple: 88% whole sentence: 93%

PP combines with the NPto form another NP

PP is an indirect object of the verb

Related Work

Supervised (Brill & Resnik, 94):

transformation-based learning, WordNet classes, P=82%

(Ratnaparkhi & al., 94): ME, word classes (MI),

P=81.6% (Collins & Brooks, 95): back-off, P=84.5% (Stetina & Makoto, 97):

decision trees, WordNet, P=88.1%

(Toutanova & al., 04): morphology, syntax, WordNet, P=87.5%

(Olteanu & Moldovan, 05): in context, parser, FrameNet,

Web, SVM, P=92.85%

Unsupervised (Hindle & Rooth, 93): partially

parsed corpus, lexical associations over subsets of (v,n1,p), P=80%,R=80%

(Ratnaparkhi, 98): POS tagged corpus, unambiguous cases for (v,n1,p), (n1,p,n2), classifier: P=81.9%

(Pantel & Lin,00): collocation database, dependency parser, large corpus (125M words), P=84.3%

Unsup. state-of-the-art

Ra

tna

par

khi

dat

ase

t

Related Work: Web Unsup.

(Volk, 00): Altavista, NEAR operator, German, compare Pr(p|n1) vs. Pr(p|v), P=75%, R=58%

(Volk, 01): Altavista, NEAR operator, German, inflected queries, Pr(p,n2|n1) vs. Pr(p,n2|v), P=75%, R=85%

(Calvo & Gelbukh, 03): exact phrases, Spanish, P=91.97%, R=89.5%

(Lapata & Keller,05): Web n-grams, English, Ratnaparkhi dataset, P in low 70’s

(Olteanu & Moldovan, 05): supervised, English, in context, parser, FrameNet, Web counts, SVM, P=92.85%

PP-attachment: Our Approach

Unsupervised (v,n1,p,n2) quadruples, Ratnaparkhi test set Google and MSN Search Exact phrase queries Inflections: WordNet 2.0 Adding determiners where appropriate Models:

n-gram association models Web-derived surface features paraphrases

Probabilities: Estimation

Using page hits as a proxy for n-gram counts

Pr(w1|w2) = #(w1,w2) / #(w2) #(w2) word frequency; query for “w2”

#(w1,w2) bigram frequency; query for “w1 w2”

Pr(w1,w2|w3) = #(w1,w2,w3) / #(w3)

N-gram models

(i) Pr(p|n1) vs. Pr(p|v) (ii) Pr(p,n2|n1) vs. Pr(p,n2|v)

I eat/v spaghetti/n1 with/p a fork/n2. I eat/v spaghetti/n1 with/p sauce/n2.

Pr or # (frequency) smoothing as in (Hindle & Rooth, 93) back-off from (ii) to (i)

N-grams unreliable, if n1 or n2 is a pronoun. MSN Search: no rounding of n-gram estimates

Web-derived Surface Features

Example features open the door / with a key verb (100.00%, 0.13%) open the door (with a key) verb (73.58%, 2.44%) open the door – with a key verb (68.18%, 2.03%) open the door , with a key verb (58.44%, 7.09%)

eat Spaghetti with sauce noun (100.00%, 0.14%) eat ? spaghetti with sauce noun (83.33%, 0.55%) eat , spaghetti with sauce noun (65.77%, 5.11%) eat : spaghetti with sauce noun (64.71%, 1.57%)

Summing achieves high precision, low recall.

P R

su

ms

um

compare

Paraphrases

v n1 p n2

v n2 n1 (noun) v p n2 n1 (verb) p n2 * v n1 (verb) n1 p n2 v (noun) v PRONOUN p n2 (verb) BE n1 p n2 (noun)

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

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.

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

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

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

Paraphrases: pattern (6)

(6) v n1 p n2 BE n1 p n2 (noun)

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

to be

Evaluation

Ratnaparkhi dataset 3097 test examples, e.g.

prepare dinner for family Vshipped crabs from province V

n1 or n2 is a bare determiner: 149 examples problem for unsupervised methodsleft chairmanship of the Nis the of kind Nacquire securities for an N

special symbols: %, /, & etc.: 230 examples problem for Web queriesbuy % for 10 Vbeat S&P-down from % Vis 43%-owned by firm N

Results

Simpler but not significantlydifferent from 84.3%(Pantel&Lin,00).

For prepositions other then OF.(of noun attachment)Smoothing is not

needed on the Web

Models in bold are combined in a majority vote.

Checking directly for...

Task 2:

Coordination

Coordination & Problems (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.

Problems: boundaries: words, constituents, clauses etc. interactions with PPs: [health and [quality of life]]

vs. [[health and quality] of life]

or meaning and: chronic diseases or disabilities ellipsis

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

NC Coordination: ellipsis

Quadruple (n1,c,n2,h) Penn Treebank annotations

ellipsis:

(NP car/NN and/CC truck/NN production/NN). no ellipsis:

(NP (NP president/NN) and/CC (NP chief/NN executive/NN))

all-way: can be annotated either way This is a problem a parser must deal with.

Collins’ parser always predicts ellipsis,but other parsers (e.g. Charniak’s) try to solve it.

Related Work

(Resnik, 99): similarity of form and meaning, conceptual association, decision tree, P=80%, R=100%

(Rus & al., 02): deterministic, rule-based bracketing in context, P=87.42%, R=71.05%

(Chantree & al., 05): distributional similarities from BNC, Sketch Engine (freqs., object/modifier etc.), P=80.3%, R=53.8%

(Goldberg, 99): different problem (n1,p,n2,c,n3), adapts Ratnaparkhi (99) algorithm, P=72%, R=100%

N-gram models

(n1,c,n2,h)

(i) #(n1,h) vs. #(n2,h) (ii) #(n1,h) vs. #(n1,c,n2)

Surface Featuress

um

su

m

compare

Paraphrases

n1 c n2 h

n2 c n1 h (ellipsis) n2 h c n1 (NO ellipsis) n1 h c n2 h (ellipsis) n2 h c n1 h (ellipsis)

Paraphrases: Pattern (1)

(1) n1 c n2 h n2 c n1 h (ellipsis)

Switch places of n1 and n2 bar/n1 and/c pie/n2 graph/h pie/n2 and/c bar/n1 graph/h

Paraphrases: Pattern (2)

(2) n1 c n2 h n2 h c n1 (NO ellipsis)

Switch places of n1 and n2 h president/n1 and/c chief/n2 executive/h chief/n2 executive/h and/c president/n1

Paraphrases: Pattern (3)

(3) n1 c n2 h n1 h c n2 h (ellipsis)

Insert the elided head h bar/n1 and/c pie/n2 graph/h bar/n1 graph/h and/c pie/n2 graph/h

h

Paraphrases: Pattern (4)

(4) n1 c n2 h n2 h c n1 h (ellipsis)

Insert the elided head h, but also switch n1 and n2 bar/n1 and/c pie/n2 graph/h pie/n2 graph/h and/c bar/n1 graph/h

h

(Rus & al.,02) Heuristics

Heuristic 1: no ellipsis n1=n2 milk/n1 and/c milk/n2 products/h

Heuristic 4: no ellipsis n1 and n2 are modified by an adjective

Heuristic 5: ellipsis only n1 is modified by an adjective

Heuristic 6: no ellipsis only n2 is modified by an adjective

We use a determiner.

Number Agreement

Introduced by Resnik (93)(a) n1&n2 agree, but n1&h do not ellipsis;

(b) n1&n2 don’t agree, but n1&h do no ellipsis;

(c) otherwise leave undecided.

Results428 examples from Penn TB

Models in bold are combined in a majority vote.

Comparable to other researchers (but no standard dataset).

Bad, compares bigram to trigram.

Conclusions & Future Work

Tapping the potential of very large corpora for unsupervised algorithms Go beyond n-grams

Surface features Paraphrases

Results competitive with best unsupervised Results can rival supervised algorithms’

Future Work other NLP tasks better evidence combination

There should be even more exciting features on the Web!

The End

Thank you!