Medical imaging plays a significant role in clinical practice of medical diagnosis, where the
text reports of the images are essential in understanding them and facilitating later …

Medical imaging is frequently used in clinical practice and trials for diagnosis and treatment.
Writing imaging reports is time-consuming and can be error-prone for inexperienced …

Lexicon information and pre-trained models, such as BERT, have been combined to explore
Chinese sequence labelling tasks due to their respective strengths. However, existing …

The pre-training of text encoders normally processes text as a sequence of tokens
corresponding to small text units, such as word pieces in English and characters in Chinese …

Existing approaches for named entity recognition suffer from data sparsity problems when
conducted on short and informal texts, especially user-generated social media content …

End-to-end aspect-based sentiment analysis (EASA) consists of two sub-tasks: the first
extracts the aspect terms in a sentence and the second predicts the sentiment polarities for …

Large pre-trained models such as BERT are known to improve different downstream NLP
tasks, even when such a model is trained on a generic domain. Moreover, recent studies …

Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic
properties where entities may be extracted according to how they are used and placed in the …

Summarization is an important natural language processing (NLP) task in identifying key
information from text. For conversations, the summarization systems need to extract salient …

Aspect-level sentiment analysis (ASA) has received much attention in recent years. Most
existing approaches tried to leverage syntactic information, such as the dependency parsing …