Scaling up Analogical Learning

Recent years have witnessed a growing interest in analogical learning for NLP applications. If the principle of analogical learning is quite simple, it does involve complex steps that seriously limit its applicability, the most computationally demanding one being the identification of analogies in the input space. In this study, we investigate different strategies for efficiently solving this problem and study their scalability.

• Type: Conference
• Year: 2008
• Where: COLING
• Authors: Philippe Langlais, François Yvon
• Keywords: Analogical Learning, COLING 2008, Complex Steps, Computational Linguistics, NLP Applications