Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Using the web to obtain frequencies for unseen bigrams
Computational Linguistics - Special issue on web as corpus
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Web-based models for natural language processing
ACM Transactions on Speech and Language Processing (TSLP)
Corpus-based Learning of Analogies and Semantic Relations
Machine Learning
Using the web as an implicit training set: application to structural ambiguity resolution
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Models for the semantic classification of noun phrases
CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics
Using verbs to characterize noun-noun relations
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
Using lexical patterns in the Google Web 1T corpus to deduce semantic relations between nouns
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
Automatic discovery of manner relations and its applications
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Unsupervised learning of semantic relation composition
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
A model for composing semantic relations
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Composition of semantic relations: Theoretical framework and case study
ACM Transactions on Speech and Language Processing (TSLP)
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This paper investigates the use of machine learning algorithms to label modifier-noun compounds with a semantic relation. The attributes used as input to the learning algorithms are the web frequencies for phrases containing the modifier, noun, and a prepositional joining term. We compare and evaluate different algorithms and different joining phrases on Nastase and Szpakowicz's (2003) dataset of 600 modifier-noun compounds. We find that by using a Support Vector Machine classifier we can obtain better performance on this dataset than a current state-of-the-art system; even with a relatively small set of prepositional joining terms.