A survey on coreference resolution

doi:10.3969/j.issn.1000-5641.2019.05.002

Abstract

Abstract: 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.

Key words: coreference resolution, natural language processing, global optimization, knowledge base, deep learning

CLC Number:

TP391

CHEN Yuan-zhe, KUANG Jun, LIU Ting-ting, GAO Ming, ZHOU Ao-ying. A survey on coreference resolution[J]. Journal of East China Normal University(Natural Sc, 2019, 2019(5): 16-35.

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