On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Automatic labeling of semantic roles
Computational Linguistics
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Labeling chinese predicates with semantic roles
Computational Linguistics
SemEval'07 task 19: frame semantic structure extraction
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Semantic role labelling with tree conditional random fields
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
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Given an input sentence and a target word and its frame, automatic semantic role labeling on the Chinese FrameNet (CFN) database can be divided into two subtasks. One is identification of the boundaries of semantic roles, and the other is the classification of semantic roles. In this paper, the tasks are regarded as a sequential tagging problem in the sentence at word-level, so the model of conditional random fields was adopted. The best feature templates of the model were chosen by applying orthogonal arrays. The training and testing data sets consist of the sample sentences of 25 frames selected from the current CFN corpus. The experimental results in our test data set show that the F-measure for identifying boundaries of semantic roles reached 70.42\%, and the accuracy in classifying semantic roles achieved 80.4\%, but when the two subtasks were sequential automatically processed, the result was 59.48\% F-measure.