A Nested Named Entity Recognition Model Based on Multi-agent Communication Mechanism (Student Abstract)
| Autor: | Canguang Li, Guohua Wang, Jin Cao, Yi Cai |
|---|---|
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Proceedings of the AAAI Conference on Artificial Intelligence. 35:15823-15824 |
| ISSN: | 2374-3468 2159-5399 |
| DOI: | 10.1609/aaai.v35i18.17908 |
| Popis: | Traditional sequence tagging methods for named entity recognition (NER) face challenges when handling nested entities, where an entity is nested in another. Most previous methods for nested NER ignore the effect of entity boundary information or type information. Considering that entity boundary information and type information can be utilized to improve the performance of boundary detection, we propose a nested NER model with a multi-agent communication module. The type tagger and boundary tagger in the multi-agent communication module iteratively utilize the information from each other, which improves the boundary detection and the final performance of nested NER. Empirical experiments conducted on two nested NER datasets show the effectiveness of our model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|