{"type":"ScholarlyArticle","title":"Determining Expert Research Areas with Multi-Instance Learning of Hierarchical Multi-Label Classification Model","authors":[{"name":"Tao Wu","slug":"tao-wu","given":"Tao","family":"Wu","affiliation":""},{"name":"Qifan Wang","slug":"qifan-wang","given":"Qifan","family":"Wang","affiliation":""},{"name":"Zhiwei Zhang","slug":"zhiwei-zhang","given":"Zhiwei","family":"Zhang","affiliation":""},{"name":"Luo Si","slug":"luo-si","given":"Luo","family":"Si","affiliation":""}],"abstract":"Automatically identifying the research areas of academic/industry researchers is an important task for building expertise organizations or search systems. In general, this task can be viewed as text classification that generates a set of research areas given the expertise of a researcher like documents of publications. However, this task is challenging because the evidence of a research area may only exist in a few documents instead of all documents. Moreover, the research areas are often organized in a hierarchy, which limits the effectiveness of existing text categorization methods. This paper proposes a novel approach, Multi-instance Learning of Hierarchical Multi-label Classification Model (MIHML) for the task, which effectively identifies multiple research areas in a hierarchy from individual documents within the profile of a researcher. An Expectation-Maximization (EM) optimization algorithm is designed to learn the model parameters. Extensive experiments have been conducted to demonstrate the superior performance of proposed research with a real world application.","doi":null,"url":"https://ijcai.org/proceedings/2015/326","pdf":"https://ijcai.org/proceedings/2015/0326.pdf","markdown":"https://ijcai.org/proceedings/2015/326.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/326","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Web and Knowledge-Based Information Systems","track":"Main Track","number":326,"pages":{"first":2305,"last":2311},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}