Candidate entity generation in lexical semantics

Candidate entity generation plays a pivotal role in various Natural Language Processing tasks, particularly in lexical semantics, where identifying and selecting relevant entities is crucial for effective understanding and processing of text. This article provides an in-depth review of the use of candidate generation in key lexical semantics, including Named Entity Recognition and Disambiguation, Automatic Term Extraction, and Word Sense Disambiguation. This article examines the challenges with candidate entity generation accuracy, selection methods, and how candidate entity generation improves overall performance. Additionally, we review emerging trends, advancements, and the potential impact of candidate entity generation on lexical semantics and Natural Language Processing applications. Through this study, we conclude that advanced contextual models for candidate entity generation extensively incorporate statistical approaches along with deep contextual embeddings and attention-based mechanisms. We also find that integrating structured knowledge sources, including knowledge graphs, external databases, and ontologies, can significantly improve the performance of candidate entity generation systems. However, optimizing candidate lists while ensuring the consistent inclusion of ground truth entities for lexical semantics remains a central challenge.