TY - GEN
T1 - Minimally supervised classification to semantic categories using automatically acquired symmetric patterns
AU - Schwartz, Roy
AU - Reichart, Roi
AU - Rappoport, Ari
PY - 2014
Y1 - 2014
N2 - 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-ofthe- 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.
AB - 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-ofthe- 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.
UR - http://www.scopus.com/inward/record.url?scp=84944130629&partnerID=8YFLogxK
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AN - SCOPUS:84944130629
T3 - COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014: Technical Papers
SP - 1612
EP - 1623
BT - COLING 2014 - 25th International Conference on Computational Linguistics, Proceedings of COLING 2014
PB - Association for Computational Linguistics, ACL Anthology
T2 - 25th International Conference on Computational Linguistics, COLING 2014
Y2 - 23 August 2014 through 29 August 2014
ER -