Context-dependent word similarity can be measured over multiple cross-cutting dimensions. For example, lung and breath are similar thematically, while authoritative and superficial occur in similar syntactic contexts, but share little semantic similarity. Both of these notions of similarity play a role in determining word meaning, and hence lexical semantic models must take them both into account. Towards this end, we develop a novel model, Multi-View Mixture (MVM), that represents words as multiple overlapping clusterings. MVM finds multiple data partitions based on different subsets of features, subject to the marginal constraint that feature subsets are distributed according to Latent Dirichlet Allocation. Intuitively, this constraint favors feature partitions that have coherent topical semantics. Furthermore, MVM uses soft feature assignment, hence the contribution of each data point to each clustering view is variable, isolating the impact of data only to views where they assign the most features. Through a series of experiments, we demonstrate the utility of MVM as an inductive bias for capturing relations between words that are intuitive to humans, outperforming related models such as Latent Dirichlet Allocation.

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In Proceedings of The Conference on Empirical Methods in Natural Language Processing (EMNLP 2011), pp. 1405-1415, July 2011.

@inproceedings{reisinger.emnlp11, title={Cross-Cutting Models of Lexical Semantics}, author={Joseph Reisinger and Raymond Mooney}, booktitle={Proceedings of The Conference on Empirical Methods in Natural Language Processing (EMNLP 2011)}, month={July}, pages={1405-1415}, url="http://www.cs.utexas.edu/users/ai-lab?reisinger:emnlp11", year={2011} }

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