Investigations

Current chemical concept recognition tools have demonstrated significantly lower performance for in full-text articles than in abstracts. Improving automated full-text chemical concept recognition can substantially accelerate manual indexing and curation and advance downstream NLP tasks such as relevant article retrieval. Participating in BioCreative Track NLM-Chem we focus identifying chemicals in full-text articles (i.e. named entity recognition and normalization).

In biomedical text mining, named entity recognition (NER) is an important task used to extract information from biomedical articles. Improving the NER’s performance will directly have a positive impact on extracting relations between those entities. In recent years, deep learning has become the main research direction of NER due to the development of effective models. Language transformer models like e.g. BERT are frequently used because they enable the specialisation of models by domain-specific ...