TAILIEUCHUNG - Báo cáo khoa học: "Semantic Role Labeling via FrameNet, VerbNet and PropBank"

This article describes a robust semantic parser that uses a broad knowledge base created by interconnecting three major resources: FrameNet, VerbNet and PropBank. The FrameNet corpus contains the examples annotated with semantic roles whereas the VerbNet lexicon provides the knowledge about the syntactic behavior of the verbs. We connect VerbNet and FrameNet by mapping the FrameNet frames to the VerbNet Intersective Levin classes. The PropBank corpus, which is tightly connected to the VerbNet lexicon, is used to increase the verb coverage and also to test the effectiveness of our approach. The results indicate that our model is an interesting. | 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@ moschitti@ Abstract This article describes a robust semantic parser that uses a broad knowledge base created by interconnecting three major resources FrameNet VerbNet and PropBank. The FrameNet corpus contains the examples annotated with semantic roles whereas the VerbNet lexicon provides the knowledge about the syntactic 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 coverage 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 semantic 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 Marquez 2005 on PropBank PB Kingsbury and Palmer 2002 role labeling. The systems that participated in the task were trained on the Wall Street Journal corpus WSJ and tested on portions of WSJ and Brown corpora. While the best F-measure recorded on WSJ was 80 on the Brown corpus the F-measure dropped below 70 . The most significant causes for this performance decay were highly ambiguous and unseen predicates . predicates that do not have training examples . The same problem was again highlighted by the results obtained with and without the frame information in the Senseval-3 competition Litkowski 2004 of FrameNet Johnson et al. 2003 role labeling task. When such .

• Báo cáo khoa học
• Semantic Role Labeling
• FrameNet
• VerbNet and PropBank
• báo cáo khoa học
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• xử lý ngôn ngữ tự nhiên
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• Dependency Features
• Adapting Self training
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• Past Present and Future
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• Sentence Simplification
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• Systems for Arabic
• Kernel Methods
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• Learning Predictive Structures
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• Semi Supervised
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