Principles for Organizing Semantic Relations in Large Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Classifying semantic relations in bioscience texts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
A uniform approach to analogies, synonyms, antonyms, and associations
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
The latent relation mapping engine: algorithm and experiments
Journal of Artificial Intelligence Research
Measuring semantic similarity by latent relational analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Semantic relations in information science
Annual Review of Information Science and Technology
Analogy perception applied to seven tests of word comprehension
Journal of Experimental & Theoretical Artificial Intelligence - Psychometric Artificial Intelligence
SemEval-2012 task 2: measuring degrees of relational similarity
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
| Hi-index | 0.00 |
In this paper we present our approach for assigning degrees of relational similarity to pairs of words in the SemEval-2012 Task 2. To measure relational similarity we employed lexical patterns that can match against word pairs within a large corpus of 12 million documents. Patterns are weighted by obtaining statistically estimated lower bounds on their precision for extracting word pairs from a given relation. Finally, word pairs are ranked based on a model predicting the probability that they belong to the relation of interest. This approach achieved the best results on the SemEval 2012 Task 2, obtaining a Spearman correlation of 0.229 and an accuracy on reproducing human answers to MaxDiff questions of 39.4%.