Semantic Role Labeling via FrameNet, VerbNet and PropBank

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 929–936, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Semantic Role Labeling via FrameNet, VerbNet and PropBank Ana-Maria Giuglea and Alessandro Moschitti Department of Computer Science University of Rome ”Tor Vergata” Rome, Italy agiuglea@gmail.com moschitti@info.uniroma2.it Abstract This article describes a robust seman- tic parser that uses a broad knowledge base created by interconnecting three ma- jor resources: FrameNet, VerbNet and PropBank. The FrameNet corpus con- tains the examples annotated with seman- tic roles whereas the VerbNet lexicon pro- vides the knowledge about the syntac- tic behavior of the verbs. We connect VerbNet and FrameNet by mapping the FrameNet frames to the VerbNet Intersec- tive Levin classes. The PropBank corpus, which is tightly connected to the VerbNet lexicon, is used to increase the verb cov- erage and also to test the effectiveness of our approach. The results indicate that our model is an interesting step towards the design of more robust semantic parsers. 1 Introduction During the last years a noticeable effort has been devoted to the design of lexical resources that can provide the training ground for automatic se- mantic role labelers. Unfortunately, most of the systems developed until now are confined to the scope of the resource used for training. A very recent example in this sense was provided by the CONLL 2005 shared task (Carreras and M`arquez, 2005) on PropBank (PB) (Kingsbury and Palmer, 2002) role labeling. The systems that participated in the task were trained on the Wall Street Jour- nal corpus (WSJ) and tested on portions of WSJ and Brown corpora. While the best F-measure recorded on WSJ was 80%, on the Brown cor- pus, the F-measure dropped below 70%. The most significant causes for this performance decay were highly ambiguous and unseen predicates (i.e. predicates that do not have training examples). The same problem was again highlighted by the results obtained with and without the frame infor- mation in the Senseval-3 competition (Litkowski, 2004) of FrameNet (Johnson et al., 2003) role la- beling task. When such information is not used by the systems, the performance decreases by 10 percent points. This is quite intuitive as the se- mantics of many roles strongly depends on the fo- cused frame. Thus, we cannot expect a good per- formance on new domains in which this informa- tion is not available. A solution to this problem is the automatic frame detection. Unfortunately, our preliminary experiments showed that given a FrameNet (FN) predicate-argument structure, the task of identify- ing the associated frame can be performed with very good results when the verb predicates have enough training examples, but becomes very chal- lenging otherwise. The predicates belonging to new application domains (i.e. not yet included in FN) are especially problematic since there is no training data available. Therefore, we should rely on a semantic context alternative to the frame (Giuglea and Moschitti, 2004). Such context should have a wide coverage and should be easily …

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