4 accepted papers
This study examines whether the attention scores between tokens in the BERT model significantly vary based on lexical categories during the fine-tuning process for downstream tasks. Drawing inspiration from the notion that in human language processing, syntactic and semantic information is parsed di…
Instruction Tuning on Large Language Models is an essential process for model to function well and achieve high performance in the specific tasks. Accordingly, in mainstream languages such as English, instruction-based datasets are being constructed and made publicly available. In the case of Korean…
Named Entity Recognition (NER) plays a pivotal role in medical Natural Language Processing (NLP). Yet, there has not been an open-source medical NER dataset specifically for the Korean language. To address this, we utilized ChatGPT to assist in constructing the KBMC (Korean Bio-Medical Corpus), whic…
We present pioneering research in the realm of Natural Language Processing (NLP) for the endangered Manchu language. Recognizing the critical importance of linguistic preservation, we experiment with three language models – BiLSTM-CRF, BERT, and mBERT – for Named Entity Recognition (NER) and Part-of…