Topic
Biomedical text mining research, explained
6 open-access biomedical text mining studies, each with a flashcard deck and a quiz.
- Can one model find diseases, genes and how they relate in text?
Treating relation extraction as a sequence-labelling problem with conditional random fields matched or beat older methods, and relation-specific features drove most of the gain.
- Does training on many biomedical datasets at once improve NER?
Letting one neural network learn many biomedical name-tagging datasets together gave modest average gains, larger gains on some datasets, and the biggest benefit when training data was scarce.
- Can expert models for each entity type help each other tag biomedical text?
Letting separately trained disease, chemical and gene taggers pass their predictions to one another raised precision and cut cases where one entity type was mistaken for another.
- Better tagging of gene and protein names in biology papers
Adding chosen word-pair features, replacing numbers with placeholders and applying sentence-wide patterns lifted a CRF biomedical name tagger above every system in a standard shared task.
- Does adding neighbouring-word context improve biomedical NER?
Feeding each word's neighbours into an LSTM tagger modestly beat earlier deep-learning taggers at finding disease and chemical names, but BERT still did better on genes and on cross-corpus tests.
- Does focusing on nearby words help find gene and disease names?
A convolutional network that looks only at the words around each term matched or beat most existing tools at spotting biomedical names in text, without hand-made features.