Scaling up Analogical Learning
Philippe Langlais, François Yvon · International Conference on Computational Linguistics · 2008
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.