属性抽取研究综述

Survey on Aspect Term Extraction

属性抽取是一种自动识别和提取属性表述文字的自然语言处理任务.首先重温了属性抽取的基本任务、权威数据资源和通用评测规范,并在此基础上全面回顾了现有前沿技术,包括基于统计策略和特征工程的传统抽取技术以及利用深度学习的神经抽取技术.特别地,以属性表述语言的本质为出发点,结合现有技术暴露出的不足,对该领域的技术难点和推演方向给出了详细解释.

Aspect term extraction is a natural language processing task that automatically recognizes and extracts aspect term from free expression text.The study first goes over the basic task of aspect term extraction,the authoritative datasets of it and general evaluation specifications on it.Based on these,the study comprehensively reviews on the state-of-the-art techniques for the task,including traditional extraction techniques based on statistical strategies and feature engineering,and the neural extraction techniques using deep learning.In particular,the study takes the essence of expression language as the starting point,combines with the limitations of existing techniques and gives an elaboration of the technical difficulties and the future development prospects of aspect term extraction.