{"type":"ScholarlyArticle","title":"Ranking Structured Documents: A Large Margin Based Approach for Patent Prior Art Search","authors":[{"name":"Yunsong Guo","slug":"yunsong-guo","given":"Yunsong","family":"Guo","affiliation":""},{"name":"Carla P. Gomes","slug":"carla-p-gomes","given":"Carla P.","family":"Gomes","affiliation":""}],"abstract":"We propose an approach for automatically ranking structured documents applied to patent prior art search. Our model, SVM Patent Ranking (SVM_PR) incorporates margin constraints that directly capture the specificities of patent citation ranking. Our approach combines patent domain knowledge features with meta-score features from several different general Information Retrieval methods. The training algorithm is an extension of the Pegasos algorithm with performance guarantees, effectively handling hundreds of thousands of patent-pair judgements in a high dimensional feature space. Experiments on a homogeneous essential wireless patent dataset show that SVM_PR performs on average 30%-40% better than many other state-of-the-art general-purpose Information Retrieval methods in terms of the NDCG measure at different cut-off positions.Yunsong Guo, Carla Gomes","doi":null,"url":"https://ijcai.org/proceedings/2009/179","pdf":"https://ijcai.org/proceedings/2009/0179.pdf","markdown":"https://ijcai.org/proceedings/2009/179.md","bibtex":"https://ijcai.org/proceedings/2009/bibtex/179","volume":{"slug":"2009","title":"Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2009","url":"https://ijcai.org/proceedings/2009/","year":2009,"location":"Pasadena, California, USA","dates":"","editors":"Hiroaki Kitano","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":179,"pages":{"first":1058,"last":1063},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}