Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866909435473952768 |
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| author | Keraghel, Imed Morbieu, Stanislas Nadif, Mohamed |
| author_facet | Keraghel, Imed Morbieu, Stanislas Nadif, Mohamed |
| contents | 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, including advancements in Transformer-based methods and Large Language Models (LLMs) that have not had much coverage in other surveys. In addition, we discuss reinforcement learning and graph-based approaches, highlighting their role in enhancing NER performance. 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 have never been considered together. Our experiments shed some light on how the characteristics of datasets affect the behavior of the methods we compare. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_10825 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study Keraghel, Imed Morbieu, Stanislas Nadif, Mohamed Computation and Language Machine Learning 68T50, 68Q32 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, including advancements in Transformer-based methods and Large Language Models (LLMs) that have not had much coverage in other surveys. In addition, we discuss reinforcement learning and graph-based approaches, highlighting their role in enhancing NER performance. 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 have never been considered together. Our experiments shed some light on how the characteristics of datasets affect the behavior of the methods we compare. |
| title | Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative Study |
| topic | Computation and Language Machine Learning 68T50, 68Q32 |
| url | https://arxiv.org/abs/2401.10825 |