An Analogical Learner for Morphological Analysis

Analogical learning is based on a two-step inference process: (i) computation of a structural mapping between a new and a memorized situation; (ii) transfer of knowledge from the known to the unknown situation. This approach requires the ability to search for and exploit such mappings, hence the need to properly define analogical relationships, and to efficiently implement their computation. In this paper, we propose a unified definition for the notion of (formal) analogical proportion, which applies to a wide range of algebraic structures. We show that this definition is suitable for learning in domains involving large databases of structured data, as is especially the case in Natural Language Processing (NLP). We then present experimental results obtained on two morphological analysis tasks which demonstrate the flexibility and accuracy of this approach.

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