Related Named Entities Classification in the Economic-Financial Context
Daniel De Los Reyes, Allan Barcelos, Renata Vieira, Isabel Manssour
Abstract
The present work uses the Bidirectional Encoder Representations from Transformers (BERT) to process a sentence and its entities and indicate whether two named entities present in a sentence are related or not, constituting a binary classification problem. It was developed for the Portuguese language, considering the financial domain and exploring deep linguistic representations to identify a relation between entities without using other lexical-semantic resources. The results of the experiments show an accuracy of 86% of the predictions.- Anthology ID:
- 2021.hackashop-1.2
- Volume:
- Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Hannu Toivonen, Michele Boggia
- Venue:
- Hackashop
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8–15
- Language:
- URL:
- https://aclanthology.org/2021.hackashop-1.2
- DOI:
- Cite (ACL):
- Daniel De Los Reyes, Allan Barcelos, Renata Vieira, and Isabel Manssour. 2021. Related Named Entities Classification in the Economic-Financial Context. In Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation, pages 8–15, Online. Association for Computational Linguistics.
- Cite (Informal):
- Related Named Entities Classification in the Economic-Financial Context (De Los Reyes et al., Hackashop 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.hackashop-1.2.pdf