ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer

Ebrahim Chekol Jibril*, A. Cuneyd Tantug

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Named Entity Recognition is an information extraction task that serves as a pre-processing step for other natural language processing tasks, such as machine translation, information retrieval, and question answering. Named entity recognition enables the identification of proper names as well as temporal and numeric expressions in an open domain text. For Semitic languages such as Arabic, Amharic, and Hebrew, the named entity recognition task is more challenging due to the heavily inflected structure of these languages. In this study, we annotate a new comparatively large Amharic named entity recognition dataset and make it publicly available. Using this new dataset, we build multiple Amharic named entity recognition systems based on recent deep learning approaches including transfer learning (RoBERTa), and bidirectional long short-term memory coupled with a conditional random fields layer. By applying the Synthetic Minority Over-sampling Technique to mitigate the imbalanced classification problem, our best performing RoBERTa based named entity recognition system achieves an f1-score of 93%, which is the new state-of-the-art result for Amharic named entity recognition.

Original languageEnglish
Pages (from-to)15799-15815
Number of pages17
JournalIEEE Access
Volume11
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Amharic
  • deep learning BiLSTM-CRF
  • named entity recognition
  • synthetic minority over-sampling technique
  • transfer learning

Fingerprint

Dive into the research topics of 'ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer'. Together they form a unique fingerprint.

Cite this