An Analogical Learner for Morphological Analysis

Proceedings of the 9th Conference on Computational Natural Language Learning (CoNLL), pages 120–127, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics An Analogical Learner for Morphological Analysis Nicolas Stroppa & Franc¸ois Yvon GET/ENST & LTCI, UMR 5141 46 rue Barrault, 75013 Paris, France {stroppa,yvon}@enst.fr Abstract 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 un- known situation. This approach requires the ability to search for and exploit such mappings, hence the need to properly de- fine analogical relationships, and to effi- ciently implement their computation. In this paper, we propose a unified defini- tion 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 do- mains involving large databases of struc- tured data, as is especially the case in Nat- ural Language Processing (NLP). We then present experimental results obtained on two morphological analysis tasks which demonstrate the flexibility and accuracy of this approach. 1 Introduction Analogical learning (Gentner et al., 2001) is based on a two-step inductive process. The first step con- sists in the construction of a structural mapping be- tween a new instance of a problem and solved in- stances of the same problem. Once this mapping is established, solutions for the new instance can be induced, based on one or several analogs. The im- plementation of this kind of inference process re- quires techniques for searching for, and reasoning with, structural mappings, hence the need to prop- erly define the notion of analogical relationships and to efficiently implement their computation. In Natural Language Processing (NLP), the typ- ical dimensionality of databases, which are made up of hundreds of thousands of instances, makes the search for complex structural mappings a very challenging task. It is however possible to take ad- vantage of the specific nature of linguistic data to work around this problem. Formal (surface) analog- ical relationships between linguistic representations are often a good sign of deeper analogies: a surface similarity between the word strings write and writer denotes a deeper (semantic) similarity between the related concepts. Surface similarities can of course be misleading. In order to minimize such confu- sions, one can take advantage of other specificities of linguistic data: (i) their systemic organization in (pseudo)-paradigms, and (ii) their high level of re- dundancy. In a large lexicon, we can indeed expect to find many instances of pairs like write-writer: for instance read-reader, review-reviewer... Complementing surface analogies with statistical information thus has the potential to make the search problem tractable, while still providing with many good analogs. Various attempts have been made to use surface analogies in various contexts: automatic word pronunciation (Yvon, 1999), morphological analysis (Lepage, 1999a; Pirrelli and Yvon, 1999) and syntactical analysis (Lepage, 1999b). These ex- periments have mainly focused on linear represen- 120 tations of linguistic data, taking the form of …

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