2 code implementations • 12 Aug 2020 • Qihao Zhu, Zeyu Sun, Xiran Liang, Yingfei Xiong, Lu Zhang
To address these problems, we propose a novel neural architecture named OCoR, where we introduce two specifically-designed components to capture overlaps: the first embeds identifiers by character to capture the overlaps between identifiers, and the second introduces a novel overlap matrix to represent the degrees of overlaps between each natural language word and each identifier.