We examine the combination of pattern-based and distributional similarity for the induction of semantic categories. Pattern-based methods are precise and sparse while distributional methods have a higher recall. Given these particular properties we use the prediction of distributional methods as a back-off to pattern-based similarity. Since our pattern-based approach is embedded into a semi-supervised graph clustering algorithm, we also examine how distributional information is best added to that classifier. Our experiments are carried out on 5 different food categorization tasks.
CITATION STYLE
Wiegand, M., Roth, B., & Klakow, D. (2015). Combining pattern-based and distributional similarity for graph-based noun categorization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9103, pp. 64–72). Springer Verlag. https://doi.org/10.1007/978-3-319-19581-0_5
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