Abstract
Unsupervised paraphrase acquisition has
been an active research field in recent
years, but its effective coverage and performance
have rarely been evaluated. We
propose a generic paraphrase-based approach
for Relation Extraction (RE), aiming
at a dual goal: obtaining an applicative
evaluation scheme for paraphrase acquisition
and obtaining a generic and largely
unsupervised configuration for RE. We analyze
the potential of our approach and
evaluate an implemented prototype of it
using an RE dataset. Our findings reveal a
high potential for unsupervised paraphrase
acquisition. We also identify the need for
novel robust models for matching paraphrases
in texts, which should address syntactic
complexity and variability
Original language | American English |
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Title of host publication | EACL |
State | Published - 2006 |