This paper presents a machine learning approach to the resolution of coreferential relations between nominal constituents in Dutch. It is the first significant automatic approach to the resolution of coreferential relations between nominal constituents for this language. The corpusbased strategy was enabled by the annotation of a substantial corpus (ca. 12,500 noun phrases) of Dutch news magazine text with coreferential links for pronominal, proper noun and common noun coreferences. Based on the hypothesis that different types of information sources contribute to a correct resolution of different types of coreferential links, we propose a modular approach in which a separate module is trained per NP type. 1 The task of coreference resolution Although largely unexplored for Dutch, automatic coreference 1 resolution is a research area which is becoming increasingly popular in natural language processing (NLP) research. It is a weakness and therefore a key task in applications such as machine translation, automatic summarization and information extraction for which text understanding is of crucial importance.

This paper describes an alternative approach to morphological language modeling, which incorporates constraints on the morphological production of new words. This is done by applying the constraints as a preprocessing step in which only one morphological production rule can be applied to an extended lexicon of known morphemes, lemmas and word forms. This approach is used to extend the CELEX Dutch morphological database, so that a higher coverage can be reached on a large corpus of Dutch newspaper articles. We present experimental results on the coverage of this extended database and use the extension to further evaluate our morphological system, as well as the impact of the constraints on the coverage of out-of-vocabulary words. 1.