A machine learning approach to coreference resolution of noun phrases
Computational Linguistics - Special issue on computational anaphora resolution
Optimizing algorithms for pronoun resolution
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
On coreference resolution performance metrics
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Hi-index | 0.00 |
In this paper we focus on anaphora resolution for German, a highly inflected language which also allows for closed form compounds (i.e. compounds without spaces). Especially, we describe a system that only uses real preprocessing components, e.g. a dependency parser, a two-level morphological analyser etc. We trace the performance drop occurring under these conditions back to underspecification and ambiguity at the morphological level. A demanding subtask of anaphora resolution are the so-called bridging anaphora, a special variant of nominal anaphora where the heads of the coreferent noun phrases do not match. We experiment with two different resources in order to find out how to cope best with this problem.