Minimally Supervised Classification to Semantic Categories using Automatically Acquired Symmetric Patterns


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 unsupervised 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.

In Proc. of COLING 2014