{"type":"ScholarlyArticle","title":"Exploiting Known Taxonomies in Learning Overlapping Concepts","authors":[{"name":"Lijuan Cai","slug":"lijuan-cai","given":"Lijuan","family":"Cai","affiliation":""},{"name":"Thomas Hofmann","slug":"thomas-hofmann","given":"Thomas","family":"Hofmann","affiliation":""}],"abstract":"Many real-world classification problems involve large numbers of overlapping categories that are arranged in a hierarchy or taxonomy. We propose to incorporate prior knowledge on category taxonomy directly into the learning architecture. We present two concrete multi-label classification methods, a generalized version of Perceptron and a hierarchical multi-label SVM learning. Our method works with arbitrary, not necessarily singly connected taxonomies, and can be applied more generally in settings where categories are characterized by attributes and relations that are not necessarily induced by a taxonomy. Experimental results on WIPO-alpha collection show that our hierarchical methods bring significant performance improvement.","doi":null,"url":"https://ijcai.org/proceedings/2007/113","pdf":"https://ijcai.org/proceedings/2007/0113.pdf","markdown":"https://ijcai.org/proceedings/2007/113.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/113","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":113,"pages":{"first":708,"last":712},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}