Recently, the emergence of pre-trained models (PTMs) has brought natural language
processing (NLP) to a new era. In this survey, we provide a comprehensive review of PTMs …

Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology across
diverse sectors, offering adaptable solutions to complex challenges in both military and …

Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …

Abstract Clinical Named Entity Recognition (CNER) is a critical task which aims to identify
and classify clinical terms in electronic medical records. In recent years, deep neural …

The unstructured nature of clinical notes within electronic health records often conceals vital
patient-related information, making it challenging to access or interpret. To uncover this …

The amount of archaeological literature is growing rapidly. Until recently, these data were
only accessible through metadata search. We implemented a text retrieval engine for a large …

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

Abstract Named Entity Recognition (NER) plays an important role in various Natural
Language Processing (NLP) applications to extract the key information from a huge amount …

Benchmarking state-of-the-art text classification and information extraction systems in
multilingual, cross-lingual, few-shot, and zero-shot settings for socio-political event …

We present MatSci-NLP, a natural language benchmark for evaluating the performance of
natural language processing (NLP) models on materials science text. We construct the …