Discovering word senses from text
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Empirical estimates of adaptation: the chance of two noriegas is closer to p/2 than p2
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
The design, implementation, and use of the Ngram statistics package
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
Significant lexical relationships
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Distributional semantics and compositionality 2011: shared task description and results
DiSCo '11 Proceedings of the Workshop on Distributional Semantics and Compositionality
An unsupervised ranking model for noun-noun compositionality
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
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This paper describes three systems from the University of Minnesota, Duluth that participated in the DiSCo 2011 shared task that evaluated distributional methods of measuring semantic compositionality. All three systems approached this as a problem of collocation identification, where strong collocates are assumed to be minimally compositional. duluth-1 relies on the t-score, whereas duluth-2 and duluth-3 rely on Pointwise Mutual Information (pmi). duluth-1 was the top ranked system overall in coarse--grained scoring, which was a 3-way category assignment where pairs were assigned values of high, medium, or low compositionality.