This paper embarks on an exploration into the Large Language Model (LLM) datasets,
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …

Deep neural networks and huge language models are becoming omnipresent in natural
language applications. As they are known for requiring large amounts of training data, there …

Natural language processing (NLP) is crucial in the current processing of data because it
takes into account many sources, formats, and purposes of data as well as information from …

In scientific research, the method is an indispensable means to solve scientific problems and
a critical research object. With the advancement of sciences, many scientific methods are …

The lack of word boundaries information has been seen as one of the main obstacles to
develop a high performance Chinese named entity recognition (NER) system. Fortunately …

Recent studies in deep learning have shown significant progress in named entity
recognition (NER). Most existing works assume clean data annotation, yet a fundamental …

In many scenarios, named entity recognition (NER) models severely suffer from unlabeled
entity problem, where the entities of a sentence may not be fully annotated. Through …

Named entity recognition (NER) is a key component of many scientific literature mining
tasks, such as information retrieval, information extraction, and question answering; …

Misinformation is considered a threat to our democratic values and principles. The spread of
such content on social media polarizes society and undermines public discourse by …

Large Language Models (LLMs) pre-trained on massive corpora have exhibited remarkable
performance on various NLP tasks. However, applying these models to specific domains still …