We describe two newly developed computational tools for morphological processing: a program for analysis of English inflectional morphology, and a morphological generator, automatically derived from the analyser. The tools are fast, being based on finite-state techniques, have wide coverage, incorporating data from various corpora and machine readable dictionaries, and are robust, in that they are able to deal effectively with unknown words. The tools are freely available. We evaluate the accuracy and speed of both tools and discuss a number of practical applications in which they have been put to use.

Article choice can pose difficult problems in applications such as machine translation and automated summarization. In this paper, we investigate the use of corpus data to collect statistical generalizations about article use in English in order to be able to generate articles automatically to supplement a symbolic generator. We use data from the Penn Treebank as input to a memory-based learner (TiMBL 3.0;Daelemans et al., 2000) which predicts whether to generate an article with respect to an English base noun phrase. We discuss competitive results obtained using a variety of lexical, syntactic and semantic features that play an important role in automated article generation.

In practical natural language generation systems it is often advantageous to have a separate component that deals purely with morphological processing. We present such a component: a fast and robust morphological generator for English based on finite-state techniques that generates a word form given a specification of the lemma, part-of-speech, and the type of inflection required. We describe how this morphological generator is used in a prototype system for automatic simplification of English newspaper text, and discuss practical morphological and orthographic issues we have encountered in generation of unrestricted text within this application.

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