Unsupervised Acquisition of Axioms to
Paraphrase Noun Compounds and
Genitives
CICLING 2012, New Delhi
Anselmo Peñas
NLP & IR Group, UNED, Spain
Ekaterina Ovchinnikova
USC – Information Science Institute, USA
UNED
Texts omit information

Humans optimize language generation
effort

We omit information that we know the
receptor is able to predict and recover

Our research goal is to make explicit the
omitted information in texts
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Implicit predicates

In particular, some noun compounds and
genitives are used in such way

In these cases, we want to recover the implicit
predicates

For example:
• Morning coffee -> coffee drunk in the morning
• Malaria mosquito -> mosquito that carries malaria
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How to find the candidates?

Nakov & Hearst 2006


Search the web
•N1 N2 -> N2 THAT * N1
•Malaria mosquito -> mosquito THAT * malaria
Here we use Proposition Stores




Harvest a text collection that will serve as context
Parse documents
Count N-V-N, N-V-P-N, N-P-N, … structures
Build Proposition Stores (Peñas & Hovy, 2010)
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Proposition Stores
Example: propositions that relate
Bomb, attack
•npn:[bomb:n, in:in, attack:n]:13.
•nvpn:[bomb:n, explode:v, in:in, attack:n]:11.
•nvnpn:[bomb:n, kill:v, people:n, in:in, attack:n]:8.
•npn:[attack:n, with:in, bomb:n]:8.
•…
All of them could be paraphrases for the noun
compound “bomb attack”
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NE Semantic Classes
Now, What happens if we have a Named Entity?




Shakespeare’s tragedy
-> write
Why?
Consider
• John’s tragedy
• Airbus’ tragedy
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NE Semantic Classes
We are considering the “semantic classes”
of the NE
Shakespeare -> writer
writer, tragedy -> write
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Class-Instance relations

Fortunately, relevant semantic classes are
pointed out in texts through well-known
structures
• appositions, copulative verbs, “such as”, …

Here we take advantage of dependency
parsing to get class-instance relations
NNP
NNP
nn
NN
NNP
appos
NN
be
NN
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Class-Instance relations
World News
has_instance(leader,'Yasir':'Arafat'):1491.
has_instance(spokesman,'Marlin':'Fitzwater'):1001.
has_instance(leader,'Mikhail':'S.':'Gorbachev'):980.
has_instance(chairman,'Yasir':'Arafat'):756.
has_instance(agency,'Tass'):637.
has_instance(leader,'Radovan':'Karadzic'):611.
has_instance(adviser,'Condoleezza':'Rice'):590.
…
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So far
Propositions: <p,a> | P(p,a)
p: predicate
a: list of arguments <a1 …an>
P(p,a): joint probability
Class-instance relations: <c,i> | P(c,i)
c: class
i: instance
P(c,i): joint probability
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Probability of a predicate

Let’s consider the following example
Favre pass

Assume the text has pointed out he is a
quarterback

What is Favre doing with the pass?
The same as other quarterbacks
•The quarterbacks we observed before in the
background collection – Proposition Store
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Probability of a predicate
Favre pass -> p | P(p|i)
Favre -> quarterback | P(c|i)
quarterback, pass -> throw | P(p|c)
P ( p | i )   P (c | i )  P ( p | c )
ci
We already have:
n
P(c | i)   P(ck | ik )
k 1
We need to estimate: P(p|c)
(What other quarterbacks do with passes)
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Probability of a predicate
quarterback pass -> p | P(p|c)
• Steve:Young pass -> throw | P(p|i)
• Culpepper pass -> complete | P(p|i)
•…
P( p | c)   P(i | c)  P( p | i)
ic
We already have
n
P(i | c)   P(ik | ck )
k 1
and P(p|i) comes from previous observation:
Proposition Store
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Evaluation

We want to address the following questions

Do we find the paraphrases required to enable
Textual Entailment?

Do all the noun-noun dependencies need to be
paraphrased?

How frequently NEs appear in them?
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Experimental setting

Proposition Store from
216,303 World News
 7,800,000 sentences parsed


RTE-2 (Recognizing Textual Entailment)
83 entailment decisions depend on
noun-noun paraphrases
 77 different noun-noun paraphrases

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Results
How frequently NEs appear in these pairs?


82% of paraphrases contain at least one NE
62% are paraphrasing NE-N (e.g. Vikings quarterback)
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Results
Do all the noun-noun dependencies need to be
paraphrased?


No, only 54% in our test set
Some compounds encode semantic relations such as:





12% are locative relations (e.g. New York club)
Temporal relations (e.g. April 23rd strike , Friday semi-final)
Class-instance relations (e.g. quarterback Favre)
Measure, …
Some are trivial:

27% are paraphrased with “of”
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Results

Do we find the paraphrases required to enable Textual
Entailment?
 Yes in 63% of non-trivial cases
Proposition
type
Paraphrase
NPN
Jackson trial ↔ trial against Jackson
engine problem ↔ problem with engine
NVN
U.S. Ambassador ↔ Ambassador represents the U.S.
ETA bombing ↔ ETA carried_out bombing
NVNPN
wife of Joseph Wilson ↔ wife is married to Joseph Wilson
NVPN
Vietnam veteran ↔ veteran comes from Vietnam
Shapiro’s office ↔ Shapiro work in office
Germany's people ↔ people live in Germany
Abu Musab al-Zarqawi's group ↔ group led by Abu Musab al-Zarqawi
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Results
RTE-2 pair 485: Paraphrase not found
United Nations vehicle ↔ United Nations produces vehicles
United Nations doesn’t share any class with the instances
that “produce vehicles”
Toyota vehicle -> develop, build, sell, produce, make,
export, recall, assemble, …
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Conclusions



A significant proportion of noun-noun dependencies
includes Named Entities
Some noun-noun dependencies don’t require the
retrieval of implicit predicates
The method proposed is sensitive to different Nes


Different NEs retrieve different predicates
Current work: to select the most relevant paraphrase
according to the text

We are exploring weighted abduction
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Unsupervised Acquisition of Axioms to
Paraphrase Noun Compounds and
Genitives
CICLING 2012, New Delhi
Thanks!