A maximum entropy approach to natural language processing
Computational Linguistics
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Preposition semantic classification via Penn Treebank and FrameNet
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Decision trees for sense disambiguation of prepositions: case of over
CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
SemEval-2007 task 06: word-sense disambiguation of prepositions
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
KU: word sense disambiguation by substitution
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
MELB-YB: preposition sense disambiguation using rich semantic features
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
PNNL: a supervised maximum entropy approach to word sense disambiguation
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
The necessity of combining adaptation methods
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
What's in a preposition?: dimensions of sense disambiguation for an interesting word class
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Models and training for unsupervised preposition sense disambiguation
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Spatial role labeling: Towards extraction of spatial relations from natural language
ACM Transactions on Speech and Language Processing (TSLP)
A joint model for extended semantic role labeling
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A fast, accurate, non-projective, semantically-enriched parser
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Exploiting partial annotations with EM training
WILS '12 Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure
| Hi-index | 0.00 |
In this paper, we present a supervised classification approach for disambiguation of preposition senses. We use the SemEval 2007 Preposition Sense Disambiguation datasets to evaluate our system and compare its results to those of the systems participating in the workshop. We derived linguistically motivated features from both sides of the preposition. Instead of restricting these to a fixed window size, we utilized the phrase structure. Testing with five different classifiers, we can report an increased accuracy that outperforms the best system in the SemEval task.