Floating search methods in feature selection
Pattern Recognition Letters
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
The class imbalance problem: A systematic study
Intelligent Data Analysis
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Semantic Role Labeling
Spatial role labeling: Towards extraction of spatial relations from natural language
ACM Transactions on Speech and Language Processing (TSLP)
SemEval-2012 task 3: spatial role labeling
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
SemEval-2012 task 3: spatial role labeling
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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We present a joint approach for recognizing spatial roles in SemEval-2012 Task 3. Candidate spatial relations, in the form of triples, are heuristically extracted from sentences with high recall. The joint classification of spatial roles is then cast as a binary classification over the candidates. This joint approach allows for a rich feature set based on the complete relation instead of individual relation arguments. Our best official submission achieves an F1-measure of 0.573 on relation recognition, best in the task and outperforming the previous best result on the same data set (0.500).