Abstract
We investigate the task of complex NER for the English language. The task is non-trivial due to the semantic ambiguity of the textual structure and the rarity of occurrence of such entities in the prevalent literature. Using pre-trained language models such as BERT, we obtain a competitive performance on this task. We qualitatively analyze the performance of multiple architectures for this task. All our models are able to outperform the baseline by a significant margin. Our best performing model beats the baseline F1-score by over 9%.- Anthology ID:
- 2022.semeval-1.224
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1623–1629
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.224
- DOI:
- 10.18653/v1/2022.semeval-1.224
- Cite (ACL):
- Amit Pandey, Swayatta Daw, and Vikram Pudi. 2022. Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1623–1629, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Multilinguals at SemEval-2022 Task 11: Transformer Based Architecture for Complex NER (Pandey et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.semeval-1.224.pdf
- Code
- amitpandey-research/complex_ner
- Data
- MultiCoNER