N-ary Biographical Relation Extraction using Shortest Path Dependencies

Modern question answering and summarizing systems have motivated the need for complex n-ary relation extraction systems where the number of related entities (n) can be more than two. Shortest path dependency kernels have been proven to be effective in extracting binary relations. In this work, we propose a method that employs shortest path dependency based rules to extract complex n-ary relations without decomposing a sentence into constituent binary relations. With an aim of extracting biographical entities and relations from manually annotated datasets of Australian researchers and department seminar mails, we train an information extraction system which first extracts entities using conditional random fields and then employs the shortest path dependency based rules along with semantic and syntactic features to extract n-ary affiliation relations using support vector machine. Cross validation of this method on the two datasets provides evidence that it outperforms the state-of-the-art n-ary relation extraction system by a margin of 8% F-score.