Competing Algorithm Detection from Research Papers

Abstract

We propose an unsupervised approach to extract all competing algorithms present in a given scholarly article. The algorithm names are treated as named entities and natural language processing techniques are used to extract them. All extracted entity names are linked with their respective original papers in the reference section by our novel entity-citation linking algorithm. Then these entity-citation pairs are ranked based on the number of comparison related cue-words present in the entity-citation context. We manually annotated a small subset of DBLP Computer Science conference papers and report both qualitative and quantitative results of our algorithm on it.