Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
ConceptNet — A Practical Commonsense Reasoning Tool-Kit
BT Technology Journal
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Similarity of Semantic Relations
Computational Linguistics
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Integrating pattern-based and distributional similarity methods for lexical entailment acquisition
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
A uniform approach to analogies, synonyms, antonyms, and associations
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A structured vector space model for word meaning in context
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
AnalogySpace: reducing the dimensionality of common sense knowledge
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
A mixture model with sharing for lexical semantics
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Distributional memory: A general framework for corpus-based semantics
Computational Linguistics
Bootstrapping a Game with a Purpose for Commonsense Collection
ACM Transactions on Intelligent Systems and Technology (TIST)
Domain and function: a dual-space model of semantic relations and compositions
Journal of Artificial Intelligence Research
Commonsense knowledge acquisition through children's stories
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
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We introduce a way to represent word pairs instantiating arbitrary semantic relations that keeps track of the contexts in which the words in the pair occur both together and independently. The resulting features are of sufficient generality to allow us, with the help of a standard supervised machine learning algorithm, to tackle a variety of unrelated semantic tasks with good results and almost no task-specific tailoring.