A maximum entropy approach to natural language processing
Computational Linguistics
Similarity of Semantic Relations
Computational Linguistics
Noun Compound Interpretation Using Paraphrasing Verbs: Feasibility Study
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
Learning noun-modifier semantic relations with corpus-based and WordNet-based features
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A concept-centered approach to noun-compound interpretation
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Using lexical and relational similarity to classify semantic relations
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Translation by machine of complex nominals: getting it right
MWE '04 Proceedings of the Workshop on Multiword Expressions: Integrating Processing
SemEval-2007 task 04: classification of semantic relations between nominals
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
SemEval-2010 task 9: the interpretation of noun compounds using paraphrasing verbs and prepositions
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
On the semantics of noun compounds
Computer Speech and Language
Automatic interpretation of noun compounds using wordnet similarity
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
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The automatic interpretation of semantic relations between nominals is an important subproblem within natural language understanding applications and is an area of increasing interest. In this paper, we present the system we used to participate in the SemEval 2010 Task 8 Multi-Way Classification of Semantic Relations between Pairs of Nominals. Our system, based upon a Maximum Entropy classifier trained using a large number of boolean features, received the third highest score.