BC7 NLM-Chem
Full-text chemical identification and indexing in PubMed articles. Identifying named entities is an important building block for many complex knowledge extraction tasks. Errors in identifying relevant biomedical entities is a key impediment to accurate article retrieval, classification, and further understanding of textual semantics, such as relation extraction. Chemical entities appear throughout the biomedical research literature and are one of the entity types most frequently searched in PubMed. Accurate automated identification of the chemicals mentioned in journal publications has the potential to translate to improvements in many downstream NLP tasks and biomedical fields; in the near-term, specifically in the retrieval of relevant articles, greatly assisting researchers, indexers, and curators. The NLM-CHEM track will consist of two tasks. Participants can choose to participate in either one or both. These tasks are: Chemical identification in full text: predicting all chemicals mentioned in recently published full-text articles, both span (i.e. named entity recognition) and normalization (i.e. entity linking) using MeSH. Chemical indexing prediction task: predicting which chemicals mentioned in recently published full-text articles should be indexed, i.e. appear in the listing of MeSH terms for the document.