Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
SemEval-2007 task 10: English lexical substitution task
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Emotions evoked by common words and phrases: using mechanical turk to create an emotion lexicon
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
MEANS: moving effective assonances for novice students
Proceedings of the 16th international conference on Intelligent user interfaces
Text mining for automatic image tagging
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Distributional semantics in technicolor
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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Colors have a very important role on our perception of the world. We often associate colors with various concepts at different levels of consciousnes and these associations can be relevant to many fields such as education and advertisement. However, to the best of our knowledge, there are no systematic approaches to aid the automatic development of resources encoding this kind of knowledge. In this paper, we propose three computational methods based on image analysis, language models, and latent semantic analysis to automatically associate colors to words. We compare these methods against a gold standard obtained via crowdsourcing. The results show that each method is effective in capturing different aspects of word-color associations.