WordNet is an on-line lexical reference system whose design is inspired by current

Does knowledge of language consist of mentally-represented rules? Rumelhart and McClelland have described a connectionist (parallel distributed processing) model of the acquisition of the past tense in English which successfully maps many stems onto their past tense forms, both regular (walk/walked) and irregular (go/went), and which mimics some of the errors and sequences of development of children. Yet the model contains no explicit rules, only a set of neuron-style units which stand for trigrams of phonetic features of the stem, a set of units which stand for trigrams of phonetic features of the past form, and an array of connections between the two sets of units whose strengths are modified during learning. Rumelhart and McClelland conclude that linguistic rules may be merely convenient approximate fictions and that the real causal processes in language use and acquisition must be characterized as the transfer of activation levels among units and the modification of the weights of their connections. We analyze both the linguistic and the developmental assumptions of the model in detail and discover that (1) it cannot represent certain words, (2) it cannot learn many rules, (3) it can learn rules found in no human language, (4) it cannot explain morphological and phonological regularities, (5) it cannot explain the differences between irregular and regular forms, (6) it fails at its assigned task of mastering the past tense of English, (7) it gives an incorrect explanation for two developmental phenomena: stages of overregularization of irregular forms such as bringed, and the appearance of doubly-marked forms such as ated, and (8) it gives accounts of two others (infrequent overregularization of verbs ending in t/d, and the order of acquisition of different irregula...

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Supporters of eliminative connectionism have argued for a pattern association based explanation of language learning and language processing. They deny that explicit rules and symbolic representations play any role in language processing and cognition in general. Their argument is based to a large extent on two artificial neural network (ANN) models that are claimed to be able to learn the past tenses of English verbs. (Rumelhart and McClelland, 1986; MacWhinney and Leinbach, 1991). In this article we critically review Rumelhart and McClelland's as well as MacWhinney and Leinbach's ANN-models and conclude that they do not succeed in the assigned task of learning the past tenses of English verbs. In order to answer their challenge to the symbolic processing approach, we present our Symbolic Pattern Associator (SPA) -- a general purpose pattern associator that can learn to associate arbitrary discrete patterns. We carried out several experiments with the SPA using the same set of verbs ...

this article. PRINCIPLES UNDERLYING THE CONNECTIONIST APPROACH The connectionist approach instantiates a number of computational principles that are relevant to morphological processing (see Figure 2). We discuss #ve central ones in some detail because they are important for understanding the conditions under which the approach predicts morphological effects in the absence of semantic and/or phonological similarity (for additional background on principles of connectionist modelling, see Chauvin &Rumelhart, 1995; Hertz, Krogh, &Palmer, 1991; McClelland et al., 1986; Rumelhart, Hinton, & Williams, 1986a; Smolensky, Mozer, & Rumelhart, 1996)

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Abstract. In this paper the Centering model of anaphora resolution and discourse coherence (Grosz, Joshi and Weinstein, 1983, 1995) is reformulated in terms of Optimality Theory (ot) (Prince and Smolensky 1993). One version of the reformulated model is proven to be descriptively equivalent to an earlier algorithmic statement of Centering due to Brennan, Friedman and Pollard (1987). However, the new model is stated declaratively, and makes clearer the status of the various constraints used in the theory. In the second part of the paper, the model is extended, demonstrating the advantages of the ot reformulation, and capturing formally ideas originally described by Grosz, Joshi and Weinstein. Three new applications of the extended ot Centering model are described: generation of linguistic forms from meanings, the evaluation and optimization of extended texts, and the interpretation of accented pronouns.

. We present a natural language processing (NLP) approach to automatic indexing over controlled vocabulary which accounts for term variation. The approach combines a part of speech tagger, a generator of morphologically related forms, and a shallow transformational parser. The system is applied to the French language; it is trained on newspaper articles and tested on scientific literature. Precision rate of indexing on term and variants is 97.2%. It is only slightly lower than indexing without accounting for term variation (99.7%). Recall rate of indexing on term and variants (93.4%) is much higher than recall of indexing on term occurrences only (72.4%). Conflation of term variants increases indexing coverage up to 30%. The system is a convincing example of the potential synergy between full-fledged morphological analysis and local syntactic analysis. Many details are provided on the implementation of the system. Illustrative examples of syntactic transformations for the French language are given together with the theoretical and empirical methods for their formulation. 2 CHRISTIAN JACQUEMIN AND EVELYNE TZOUKERMANN 1.

We present a corpus-based system to expand multi-word index terms using a part-of-speech tagger and a full-fledged derivational morphological system, combined with a shallow parser. The system has been applied to French. The unique contribution of the research is in using these linguistically based tools with safety filters in order to avoid the problems of degradation typically associated with derivational analysis and generation. The successful expansion and thus conflation of terms, increases indexing coverage up to 30% with precision of nearly 90% for correct identification of related terms. The fully implemented system is described with particular attention on the role of derivational morphology and phrasal relations. Results and evaluation are presented in terms of precision and recall, with an analysis and discussion of errors. This paper illustrates how natural language processing tools, when combined effectively for tasks to which they are especially suited, indicates the pote...