Ranking Structured Documents: A Large Margin Based Approach for Patent Prior Art Search
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1058-1063.
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
@inproceedings{ijcai2009p179,
title = {Ranking Structured Documents: A Large Margin Based Approach for Patent Prior Art Search},
author = {Guo, Yunsong and Gomes, Carla P.},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1058--1063},
year = {2009},
url = {https://ijcai.org/proceedings/2009/179},
}
TY - CPAPER TI - Ranking Structured Documents: A Large Margin Based Approach for Patent Prior Art Search AU - Guo, Yunsong AU - Gomes, Carla P. T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1058 EP - 1063 UR - https://ijcai.org/proceedings/2009/179 ER -Copied
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