Generating English plural determiners from semantic representations: A neural network learning approach

Abstract

In this paper, we present a model of grammatical category formation, applied to English plural determiners. We have identified a set of semantic features for the description of relevant meanings of plural definiteness. A small training set (30 sentences) was created by linguistic criteria, and a functional mapping from the semantic feature representation to the overt category of indefinite/definite article was learned. The learned function was applied to all relevant plural noun occurrences in a 10000 word corpus. The results show a high degree of correctness (97%) in category assignment. We can conclude that the identified semantic dimensions are relevant and sufficient for the category of definiteness. We also have the significant result that actually occurring uses of plural determiners can be accounted for by a small set of semantic features. In a second experiment, we generated plural determiners from textually derived semantic representations, where the target category was removed from the input. Because texts are semantically underdetermined, these representations have some degree of noise. In generation we can still assign the correct category in many cases (83%). These results can be improved in various ways. It is finally discussed, how these results can be applied to practical NLP tasks such as grammar checking.