Distinguishing complements from adjuncts using memory-based learning

Abstract

The automatic distinction between complements and adjuncts, i.e. between subcategorized and non-subcategorized constituents, is crucial for the automatic acquisition of subcategorization lexicons from corpora. In this paper we present memory-based learning experiments for the task of distinguishing complements from adjuncts. Data is extracted from the part-of-speech tagged and parsed version of the Wall Street Journal Corpus. Memory-based learning algorithms classify test instances by using the class of the most similar training instance. By providing the algorithm with different subsets of features in the data, we can explore the importance of different features. By using only syntactic information about the category itself and its neighboring constituents, we achieve an accuracy of 91.6% for the complement adjunct distinction, which corresponds to 89.7% correctly classified subcategorization frames. The error analysis shows that whereas at the level of constituents, PPs are most diff...