Paper: Probabilistic Modeling of Joint-context in Distributional Similarity

ACL ID W14-1619
Title Probabilistic Modeling of Joint-context in Distributional Similarity
Venue International Conference on Computational Natural Language Learning
Session
Year 2014
Authors

Most traditional distributional similarity models fail to capture syntagmatic patterns that group together multiple word features within the same joint context. In this work we introduce a novel generic distributional similarity scheme under which the power of probabilistic models can be leveraged to effectively model joint contexts. Based on this scheme, we implement a concrete model which utilizes probabilistic n-gram language models. Our evaluations sug- gest that this model is particularly well- suited for measuring similarity for verbs, which are known to exhibit richer syntag- matic patterns, while maintaining compa- rable or better performance with respect to competitive baselines for nouns. Fol- lowing this, we propose our scheme as a framework for future semantic similarity models...