Minimally Supervised Classification to Semantic Categories using Automatically Acquired Symmetric Patterns
Roy Schwartz, Roi Reichart, Ari Rappoport · 2014
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 this task. In experiments, our model obtains 82%-94% accuracy using as few as four labeled examples per category, emphasizing the effectiveness of simple search and representation techniques for this task. 1