Mina Mottahedin

Fixing paper assignments

  1. Please select all papers that belong to the same person.
  2. Indicate below which author they should be assigned to.
Provide a valid ORCID iD here. This will be used to match future papers to this author.
Provide the name of the school or the university where the author has received or will receive their highest degree (e.g., Ph.D. institution for researchers, or current affiliation for students). This will be used to form the new author page ID, if needed.

TODO: "submit" and "cancel" buttons here

2023

pdf bib
Minanto at SemEval-2023 Task 2: Fine-tuning XLM-RoBERTa for Named Entity Recognition on English Data
Antonia Höfer | Mina Mottahedin
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

Within the scope of the shared task MultiCoNER II our aim was to improve the recognition of named entities in English. We as team Minanto fine-tuned a cross-lingual model for Named Entity Recognition on English data and achieved an average F1 score of 51.47\% in the final submission. We found that a monolingual model works better on English data than a cross-lingual and that the input of external data from earlier Named Entity Recognition tasks provides only minor improvements. In this paper we present our system, discuss our results and analyze the impact of external data.
Search
Co-authors
Venues
Fix data