{"type":"ScholarlyArticle","title":"Leveraging Multi-Domain Prior Knowledge in Topic Models","authors":[{"name":"Zhiyuan Chen","slug":"zhiyuan-chen-2","given":"Zhiyuan","family":"Chen","affiliation":""},{"name":"Arjun Mukherjee","slug":"arjun-mukherjee","given":"Arjun","family":"Mukherjee","affiliation":""},{"name":"Bing Liu","slug":"bing-liu","given":"Bing","family":"Liu","affiliation":""},{"name":"Meichun Hsu","slug":"meichun-hsu","given":"Meichun","family":"Hsu","affiliation":""},{"name":"Malú Castellanos","slug":"malu-castellanos","given":"Malú","family":"Castellanos","affiliation":""},{"name":"Riddhiman Ghosh","slug":"riddhiman-ghosh","given":"Riddhiman","family":"Ghosh","affiliation":""}],"abstract":"Topic models have been widely used to identify topics in text corpora. It is also known that purely unsupervised models often result in topics that are not comprehensible in applications. In recent years, a number of knowledge-based models have been proposed, which allow the user to input prior knowledge of the domain to produce more coherent and meaningful topics. In this paper, we go one step further to study how the prior knowledge from other domains can be exploited to help topic modeling in the new domain. This problem setting is important from both the application and the learning perspectives because knowledge is inherently accumulative. We human beings gain knowledge gradually and use the old knowledge to help solve new problems. To achieve this objective, existing models have some major difficulties. In this paper, we propose a novel knowledge-based model, called MDK-LDA, which is capable of using prior knowledge from multiple domains. Our evaluation results will demonstrate its effectiveness.","doi":null,"url":"https://ijcai.org/proceedings/2013/306","pdf":"https://ijcai.org/proceedings/2013/0306.pdf","markdown":"https://ijcai.org/proceedings/2013/306.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/306","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":306,"pages":{"first":2071,"last":2076},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}