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by Walter Daelemans, Steven Gillis, Gert Durieux
Enschede. Twente University
http://cnts.uia.ac.be/~walter/papers/1994/dgd94.ps
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Abstract:
We provide a qualitative and empirical comparison of Skousen's Analogical Modeling algorithm (AM) with Lazy Learning (LL) on a typical Natural Language Processing task. AM incorporates an original approach to feature selection and to the handling of symbolic, unordered feature values. More specifically, it provides a method to dynamically compute an optimally-sized set of nearest neighbours (the analogical set) for each test item, on the basis of which the most plausible category can be selected. We investigate the algorithm's generalisation accuracy and its tolerance to noise and compare it to Lazy Learning techniques on a primary stress assignment task in Dutch. The latter problem is typical for a large amount of classification problems in Natural Language Processing. It is shown that AM is highly successful in performing the task: it outperforms Lazy Learning in its basic scheme. However, LL can be augmented so that it performs at least as well as AM and becomes as noise tolerant as well. Keywords: Analogy-based NLP, Example- and Memory-based NLP, Statistical methods. 1
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