Exploring the Landscape of Natural Language Processing Research

Abstract

As an efficient approach to understand, generate, and process natural language texts, research in natural language processing (NLP) has exhibited a rapid spread and wide adoption in recent years. Given the increasing research work in this area, several NLP-related approaches have been surveyed in the research community. However, a comprehensive study that categorizes established topics, identifies trends, and outlines areas for future research remains absent. Contributing to closing this gap, we have systematically classified and analyzed research papers in the ACL Anthology. As a result, we present a structured overview of the research landscape, provide a taxonomy of fields of study in NLP, analyze recent developments in NLP, summarize our findings, and highlight directions for future work.

arXiv commentExtended version of the paper accepted to the 14th International Conference on Recent Advances in Natural Language Processing (RANLP 2023)

References in corpus (8)

• Efficient Estimation of Word Representations in Vector Space
• Decoupled Weight Decay Regularization
• Training language models to follow instructions with human feedback
• Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
• Scaling Laws for Neural Language Models
• Emergent Abilities of Large Language Models
• Self-Consistency Improves Chain of Thought Reasoning in Language Models
• A Survey of the State of Explainable AI for Natural Language Processing

Cited by in corpus (2)

• Efficient Domain Adaptation of Sentence Embeddings Using Adapters
• AspectCSE: Sentence Embeddings for Aspect-based Semantic Textual Similarity Using Contrastive Learning and Structured Knowledge