The advancement of machine learning and symbolic approaches have underscored their strengths and weaknesses in Natural Language Processing (NLP). While machine learning approaches are powerful in identifying patterns in data, they often fall short in learning commonsense and the factual knowledge required for the NLP tasks. Meanwhile, the symbolic methods excel in representing knowledge-rich data. However, they struggle to adapt dynamic data and generalize the knowledge. Bridging these two paradigms through hybrid approaches enables the alleviation of weaknesses in both while preserving their strengths. Recent studies extol the virtues of this union, showcasing promising results in a wide range of NLP tasks. In this paper, we present an overview of hybrid approaches used for NLP. Specifically, we delve into the state-of-the-art hybrid approaches used for a broad spectrum of NLP tasks requiring natural language understanding, generation, and reasoning. Furthermore, we discuss the existing resources available for hybrid approaches for NLP along with the challenges, offering a roadmap for future directions.

翻译：机器学习与符号方法的进步凸显了它们在自然语言处理（NLP）中的优势与不足。机器学习方法虽擅长从数据中识别模式，但在学习常识和NLP任务所需的事实性知识方面往往存在局限。而符号方法则在表达知识密集型数据方面表现出色，但在适应动态数据和知识泛化方面面临困难。通过混合方法弥合这两种范式，能够在保持双方优势的同时缓解其缺陷。近期研究盛赞这种结合的价值，在广泛的NLP任务中展现了令人瞩目的成果。本文系统梳理了用于NLP的混合方法。具体而言，我们深入探讨了针对需要自然语言理解、生成与推理的各类NLP任务所采用的最先进混合方法。此外，我们讨论了当前可用于NLP混合方法的现有资源及其面临的挑战，为未来研究方向提供了路线图。