Named entity recognition (NER) is the task of tagging entities in text with their corresponding type. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.
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This paper presents the Phenotype-Gene Relations (PGR) corpus, a silver standard corpus of human phenotype and gene annotations and their relations.
Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.
SOTA for Named Entity Recognition (NER) on BC5CDR (using extra training data)
Previous research shows that eye-tracking data contains information about the lexical and syntactic properties of text, which can be used to improve natural language processing models.
We introduce Rosita, a method to produce multilingual contextual word representations by training a single language model on text from multiple languages.
This paper presents the formal release of MedMentions, a new manually annotated resource for the recognition of biomedical concepts.
We present a framework to train a structured prediction model by performing smoothing on the inference algorithm it builds upon.
In massively multilingual transfer NLP models over many source languages are applied to a low-resource target language.
However, as deep learning models require a large amount of training data, applying deep learning to biomedical text mining is often unsuccessful due to the lack of training data in biomedical fields.
Annotated datasets in different domains are critical for many supervised learning-based solutions to related problems and for the evaluation of the proposed solutions.