Paper: Minimally Supervised Classification to Semantic Categories using Automatically Acquired Symmetric Patterns

ACL ID C14-1153
Title Minimally Supervised Classification to Semantic Categories using Automatically Acquired Symmetric Patterns
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

Classifying nouns into semantic categories (e.g., animals, food) is an important line of research in both cognitive science and natural language processing. We present a minimally supervised model for noun classification, which uses symmetric patterns (e.g., ?X and Y?) and an iterative variant of the k-Nearest Neighbors algorithm. Unlike most previous works, we do not use a predefined set of symmetric patterns, but extract them automatically from plain text, in an unsu- pervised manner. We experiment with four semantic categories and show that symmetric patterns constitute much better classification features compared to leading word embedding methods. We further demonstrate that our simple k-Nearest Neighbors algorithm outperforms two state-of- the-art label propagation alternatives for th...