---
title: "Ranking Structured Documents: A Large Margin Based Approach for Patent Prior Art Search"
authors:
  - "Yunsong Guo"
  - "Carla P. Gomes"
doi: ""
url: "https://ijcai.org/proceedings/2009/179"
pdf: "https://ijcai.org/proceedings/2009/0179.pdf"
venue: "Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence"
year: 2009
pages: "1058-1063"
track: ""
keywords:
---

# Ranking Structured Documents: A Large Margin Based Approach for Patent Prior Art Search

Yunsong Guo, Carla P. Gomes

*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