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 …

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 …

Contextual features always play an important role in Chinese word segmentation (CWS).
Wordhood information, being one of the contextual features, is proved to be useful in many …

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 …

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

Chinese word segmentation (CWS) and part-of-speech (POS) tagging are two fundamental
tasks for Chinese language processing. Previous studies have demonstrated that jointly …

In the last decade, blockchain smart contracts emerged as an automated, decentralized,
traceable, and immutable medium of value exchange. Nevertheless, existing blockchain …

Boundary information is critical for various Chinese language processing tasks, such as
word segmentation, part-of-speech tagging, and named entity recognition. Previous studies …

Named entity recognition (NER) is a subfield of natural language processing (NLP) that
identifies and classifies entities from plain text, such as people, organizations, locations, and …