Tzu-Mi Lin

• National Central University

Entity linking is the task of assigning a unique identity to named entities mentioned in a text, a sort of word sense disambiguation that focuses on automatically determining a pre-defined sense for a target entity to be disambiguated. This study proposes the DGE (Dual Gloss Encoders) model for Chinese entity linking in the biomedical domain. We se...

In this paper, we describe the process of building a benchmark data set for Chinese multi-label grammatical error detection tasks, comparing the performance of 10 representative neural network models. Experimental results reveal that no matter which deep learning model is used, the performance is still limited which confirms the difficulty of the m...

This paper describes a proposed system design for Style Change Detection (SCD) tasks for PAN at CLEF 2022. We propose a unified architecture of ensemble neural networks to solve three SCD-2022 edition tasks. We fine-tune the BERT, RoBERTa and ALBERT transformers and their connecting classifiers to measure the similarity of two given paragraphs or s...

This study describes the model design of the NCUEE-NLP system for the Chinese track of the SemEval-2022 MultiCoNER task. We use the BERT embedding for character representation and train the BiLSTM-CRF model to recognize complex named entities. A total of 21 teams participated in this track, with each team allowed a maximum of six submissions. Our b...