AIMC Topic: Natural Language Processing

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A Data-Driven Model for Automated Chinese Word Segmentation and POS Tagging.

Computational intelligence and neuroscience
Chinese natural language processing tasks often require the solution of Chinese word segmentation and POS tagging problems. Traditional Chinese word segmentation and POS tagging methods mainly use simple matching algorithms based on lexicons and rule...

MalFuzz: Coverage-guided fuzzing on deep learning-based malware classification model.

PloS one
With the continuous development of deep learning, more and more domains use deep learning technique to solve key problems. The security issues of deep learning models have also received more and more attention. Nowadays, malware has become a huge sec...

ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning.

IEEE transactions on pattern analysis and machine intelligence
Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we ...

A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques.

Computational intelligence and neuroscience
: Since December 2019, the coronavirus (COVID-19) epidemic has sparked considerable alarm among the general community and significantly affected societal attitudes and perceptions. Apart from the disease itself, many people suffer from anxiety and de...

Automated Identification of Clinical Procedures in Free-Text Electronic Clinical Records with a Low-Code Named Entity Recognition Workflow.

Methods of information in medicine
BACKGROUND: Clinical procedures are often performed in outpatient clinics without prior scheduling at the administrative level, and documentation of the procedure often occurs solely in free-text clinical electronic notes. Natural language processing...

Research and Implementation of Text Generation Based on Text Augmentation and Knowledge Understanding.

Computational intelligence and neuroscience
Text generation has always been limited by the lack of corpus data required for language model (LM) training and the low quality of the generated text. Researchers have proposed some solutions, but these solutions are often complex and will greatly i...

Development of comprehensive annotation criteria for patients' states from clinical texts.

Journal of biomedical informatics
In clinical records, much of the clinical information is recorded as free text, thus necessitating the use of advanced automatic information extraction technology. The development of practical technologies requires a corpus with finer granularity ann...

AFR-BERT: Attention-based mechanism feature relevance fusion multimodal sentiment analysis model.

PloS one
Multimodal sentiment analysis is an essential task in natural language processing which refers to the fact that machines can analyze and recognize emotions through logical reasoning and mathematical operations after learning multimodal emotional feat...

A Clinical Reasoning-Encoded Case Library Developed through Natural Language Processing.

Journal of general internal medicine
IMPORTANCE: Case reports that externalize expert diagnostic reasoning are utilized for clinical reasoning instruction but are difficult to search based on symptoms, final diagnosis, or differential diagnosis construction. Computational approaches tha...