Foundations of statistical natural language processing
Foundations of statistical natural language processing
Incremental parsing with the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A fast, accurate deterministic parser for Chinese
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Semantic role labeling using dependency trees
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies
CoNLL '08 Proceedings of the Twelfth Conference on Computational Natural Language Learning
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We present a puristic approach for combining dependency parsing and semantic role labeling. In a first step, a data-driven strict incremental deterministic parser is used to compute a single syntactic dependency structure using a MEM trained on the syntactic part of the CoNLL 2008 training corpus. In a second step, a cascade of MEMs is used to identify predicates, and, for each found predicate, to identify its arguments and their types. All the MEMs used here are trained only with labeled data from the CoNLL 2008 corpus. We participated in the closed challenge, and obtained a labeled macro F1 for WSJ+Brown of 19.93 (20.13 on WSJ only, 18.14 on Brown). For the syntactic dependencies we got similar bad results (WSJ+Brown=16.25, WSJ= 16.22, Brown=16.47), as well as for the semantic dependencies (WSJ+Brown=22.36, WSJ=22.86, Brown=17.94). The current results of the experiments suggest that our risky puristic approach of following a strict incremental parsing approach together with the closed data-driven perspective of a joined syntactic and semantic labeling was actually too optimistic and eventually too puristic.