共指消解技术综述

A survey on coreference resolution

共指消解旨在识别指向同一实体的不同表述,在文本摘要、机器翻译、自动问答和知识图谱等领域有着广泛的应用.然而,作为自然语言处理中的一个经典问题,它是一个NP-Hard的问题.本文首先对共指消解的基本概念进行介绍,对易混淆概念进行解析,并讨论了共指消解的研究意义及难点.本文进一步归纳梳理了共指消解的发展历程,将共指消解从技术层面划分为若干阶段,并介绍了各个阶段的代表性模型,探讨了各类模型的优缺点,其中着重介绍了基于规则、基于机器学习、基于全局最优化、基于知识库和基于深度学习的模型.接着对共指消解的评测会议进行介绍,对共指消解的语料库和常用评测指标进行解释和对比分析.最后,指出了当前共指消解模型尚未解决的问题,探讨了共指消解的发展趋势.

Coreference resolution is the task of finding all expressions that point to the same entity in a text;this technique is widely used for text summarization,machine translation,question answering systems,and knowledge graphs.As a classic problem in natural language processing,it is considered NP-Hard.This paper first introduces the basic concepts of coreference resolution,analyzes some confusing concepts related thereto,and discusses the research significance and difficulties of the technique.Then,we summarize research advances in coreference resolution,divide them into stages from a technical standpoint,introduce the representative approaches for each stage,and discuss the advantages and disadvantages of various methods.The summarized approaches are five-fold: rule-based,machine learning,global optimization,knowledge base,and deep learning.Next,we introduce benchmark conferences for the problem of coreference resolution;in this context,we explain and compare their corpus and common evaluation metrics.Finally,this paper highlights the open problems for coreference resolution,and discusses trends and directions of future research.