BioWordVec: Improving Biomedical Word Embeddings with Subword Information and MeSH Ontology

Distributed word representations have
become an essential foundation for biomedical natural language processing
(BioNLP). Here we present BioWordVec: an open set of biomedical word embeddings
that combines subword information from unlabelled biomedical text with a
widely-used biomedical ontology called Medical Subject Headings (MeSH). Our
BioWordVec data contain two embedding files “bio_embedding_intrinsic” and
“bio_embedding_extrinsic”. "bio_embedding_intrinsic" is for intrinsic
tasks and used to calculate or predict semantic similarity between words, terms
or sentences. "bio_embedding_extrinsic" is for extrinsic tasks and
used as the input for various downstream NLP tasks, such as relation extraction
or text classification.Both sets are in binary format and contain 2,324,849
distinct words in total where 2,309,172 words come from the PubMed and 15,677
from MeSH. All words were converted to lowercase and the number of dimensions
is 200.