Synergizing Machine Learning & Symbolic Methods: A Survey on Hybrid Approaches to Natural Language Processing
It addresses the problem of integrating disparate AI paradigms for researchers and practitioners in NLP, but it is incremental as it surveys existing work rather than introducing new methods.
This survey paper tackles the challenge of combining machine learning and symbolic methods in NLP to leverage their complementary strengths, such as pattern recognition and knowledge representation, and provides an overview of state-of-the-art hybrid approaches across various NLP tasks.
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.