A survey on recent advances in named entity recognition
Imed Keraghel Stanislas Morbieu Mohamed Nadif
Named Entity Recognition seeks to extract substrings within a text that name
real-world objects and to determine their type (for example, whether they refer
to persons or organizations). In this survey, we first present an overview of
recent popular approaches, but we also look at graph- and transformer- based
methods including Large Language Models (LLMs) that have not had much coverage
in other surveys. Second, we focus on methods designed for datasets with scarce
annotations. Third, we evaluate the performance of the main NER implementations
on a variety of datasets with differing characteristics (as regards their
domain, their size, and their number of classes). We thus provide a deep
comparison of algorithms that are never considered together. Our experiments
shed some light on how the characteristics of datasets affect the behavior of
the methods that we compare.