Investigating a Generic Paraphrase-Based Approach for Relation Extraction

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.

• Type: Conference
• Year: 2006
• Where: EACL
• Authors: Lorenza Romano, Milen Kouylekov, Idan Szpektor, Ido Dagan, Alberto Lavelli
• Keywords: EACL 2006, Generic Paraphrase-based Approach, Natural Language Processing, Paraphrase Acquisition, Unsupervised Paraphrase Acquisition