Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
SemEval'07 task 19: frame semantic structure extraction
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
CLR: integration of FrameNet in a text representation system
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
LTH: semantic structure extraction using nonprojective dependency trees
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
UTD-SRL: a pipeline architecture for extracting frame semantic structures
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Image modeling using tree structured conditional random fields
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Automatic semantic role labeling for Chinese verbs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Semantic role labelling with tree conditional random fields
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Tree-structured conditional random fields for semantic annotation
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
FrameNet, current collaborations and future goals
Language Resources and Evaluation
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As one of the important tasks of SemEval Evaluation, Frame Semantic Structure Extraction based on the FrameNet has received much more attention in NLP field. This task is often divided into three sub-tasks: recognizing target words which are word expressions that evoke semantic frames, assigning the correct frame to them, namely, Frame Identification (FI), and for each target word, detecting and labeling the corresponding frame elements properly. Frame identification is the foundation of this task. Since the existence of links between frame semantics and syntactic features, we attempt to study FI on the basis of dependency syntax. Therefore, we adopt a tree-structured conditional random field (T-CRF) model to solve Chinese frame identification based on Dependency Parsing. 7 typical lexical units which belong to more than one frame in Chinese FrameNet were selected to be researched. 940 human annotated sentences serve as the training data, and evaluation on 128 test data achieved 81.46% precision. Compared with previous works, our result shows obvious improvement.