  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 and future directions, offering a roadmap for
future research avenues.
