An Empirical Comparison of Unknown Word Prediction Methods

We compare two types of methods which deal with unknown words in the context of computational grammars. Methods of the first type are based on the idea of supertagging and use a tagger to predict lexical descriptions for unknown tokens in a given input. The second type of methods perform lexical acquisition (LA) which, in the context of this paper, refers to the automatic acquisition of new lexical entries for the lexicon of a given grammar. The methods are compared based on the effect their application has on the parsing coverage and accuracy of the GG grammar of German (Crysmann, 2003). In particular, we adapt the LA method of Cholakov and van Noord (2010) which was originally developed for the Dutch Alpino system to be used with the GG. Its impact on coverage and accuracy on a test corpus of German newspaper texts is compared to the results reported previously on the same corpus for methods which employed a tagger. Furthermore, in a smaller experiment, we show that the linguistic knowledge this LA method provides can also be used for sentence realisation.