@inproceedings{WiegandRothKlakow2019, author = {Michael Wiegand and Benjamin Roth and Dietrich Klakow}, title = {Combining Pattern-Based and Distributional Similarity for Graph-Based Noun Categorization}, series = {Natural Language Processing and Information Systems. Proceedings of the 20th International Conference on Applications of Natural Language to Information Systems, NLDB 2015, Passau, Germany, June 17–19, 2015}, editor = {Chris Biemann and Siegfried Handschuh and Andr{\´e} Freitas and Farid Meziane and Elisabeth M{\´e}tais}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-19580-3}, doi = {10.1007/978-3-319-19581-0\_5}, url = {https://nbn-resolving.org/urn:nbn:de:bsz:mh39-87479}, pages = {64 -- 72}, year = {2019}, abstract = {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.}, language = {en} }