Bidirectional Recursive Neural Networks for Token-Level Labeling with Structure.
CoRR(2013)
摘要
Recently, deep architectures, such as recurrent and recursive neural networks have been successfully applied to various natural language processing tasks. Inspired by bidirectional recurrent neural networks which use representations that summarize the past and future around an instance, we propose a novel architecture that aims to capture the structural information around an input, and use it to label instances. We apply our method to the task of opinion expression extraction, where we employ the binary parse tree of a sentence as the structure, and word vector representations as the initial representation of a single token. We conduct preliminary experiments to investigate its performance and compare it to the sequential approach.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络