Negation Detection

It is a freely available resource for research on handling negation and uncertainty in biomedical texts . The corpus consists of three parts, namely medical free texts,biological full papers and biological scientific abstracts. The dataset contains annotations at the token level for negative and speculative keywords and at the sentence level for their linguistic scope. The annotation process was carried out by two independent linguist annotators and a chief annotator – also responsible for setting up the annotation guidelines – who resolved cases where the annotators disagreed.

EMC Dutch clinical corpus contains four types of anonymized clinical documents: entries from general practitioners, specialists’ letters, radiology reports, and discharge letters. The identified UMLS terms in the corpus are annotated for negation, temporality, and experiencer properties. Zubair Afzal, Ewoud Pons, Ning Kang, Miriam CJM Sturkenboom, Martijn J Schuemie, Jan A Kors. ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus. BMC Bioinformatics 2014, 15:373 doi:10.1186/s12859-014-0373-3

We introduce a large semi-automatically generated dataset of ~400,000 descriptive sentences about commonsense knowledge that can be true or false in which negation is present in about 2/3 of the corpus in different forms that we use to evaluate LLMs. Source: ThisIsNotaDataset Image: ThisIsNotaDataset