Accepted Papers

Mining Algorithm Roadmap in Scientific Publications

Hanwen Zha (University of California, Santa Barbara);Wenhu Chen (University of California, Santa Barbara);Keqian Li (University of California, Santa Barbara);Xifeng Yan (University of California, Santa Barbara);

The number of scientific publications is ever increasing. The long time to digest a scientific paper posts great challenges on the number of papers people can read, which impedes a quick grasp of major activities in new research areas especially for intelligence analysts and novice researchers. To accelerate such a process, we first define a new problem called mining algorithm roadmap in scientific publications, and then propose a new weakly supervised method to build the roadmap. The algorithm roadmap describes evolutionary relation between different algorithms, and sketches the undergoing research and the dynamics of the area. It is a tool for analysts and researchers to locate the successors and families of algorithms when analyzing and surveying a research field. We first propose abbreviated words as candidates for algorithms and then use tables as weak supervision to extract these candidates and labels. Next we propose a new method called Cross-sentence Attention NeTwork for cOmparative Relation (CANTOR) to extract comparative algorithms from text. Finally, we derive order for individual algorithm pairs with time and frequency to construct the algorithm roadmap. Through comprehensive experiments, our proposed algorithm shows its superiority over the baseline methods on the proposed task.


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