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Leveraging Token-Based Concept Information and Data Augmentation in Few-Resource NER: ZuKyo-EN at the NTCIR-16 Real-MedNLP task


Cornelius, Joseph; Lithgow-Serrano, Oscar; Kanjirangat, Vani; Rinaldi, Fabio; Fujimoto, Koji; Nishio, Mizuho; Sugiyama, Osamu; Ichikawa, Kana; Nooralahzadeh, Farhad; Horvath, Aron N; Krauthammer, Michael (2022). Leveraging Token-Based Concept Information and Data Augmentation in Few-Resource NER: ZuKyo-EN at the NTCIR-16 Real-MedNLP task. In: NTCIR 16 Conference: Proceedings of the 16th NTCIR Conference on Evaluation of Information Access Technologies, Tokyo, Japan, 14 June 2022 - 17 June 2022, NTCIR.

Abstract

In this paper, we discuss our contribution to the NII Testbeds and Community for Information Access Research (NTCIR) - 16 Real- MedNLP shared task. Our team (ZuKyo) participated in the English subtask: Few-resource Named Entity Recognition. The main challenge in this low-resource task was a low number of training documents annotated with a high number of tags and attributes. For our submissions, we used different general and domain-specific transfer learning approaches in combination with multiple data augmentation methods. In addition, we experimented with models enriched with biomedical concepts encoded as token-based input features

Abstract

In this paper, we discuss our contribution to the NII Testbeds and Community for Information Access Research (NTCIR) - 16 Real- MedNLP shared task. Our team (ZuKyo) participated in the English subtask: Few-resource Named Entity Recognition. The main challenge in this low-resource task was a low number of training documents annotated with a high number of tags and attributes. For our submissions, we used different general and domain-specific transfer learning approaches in combination with multiple data augmentation methods. In addition, we experimented with models enriched with biomedical concepts encoded as token-based input features

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Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Department of Quantitative Biomedicine
Dewey Decimal Classification:610 Medicine & health
Language:English
Event End Date:17 June 2022
Deposited On:25 Aug 2022 11:12
Last Modified:27 Oct 2023 14:21
Publisher:NTCIR
OA Status:Green
  • Content: Published Version