BioWordVec, improving biomedical word embeddings with subword information and MeSH | Scientific Data

Design Type(s) data transformation objective • data integration objective • text processing and analysis objective Measurement Type(s) word representation Technology Type(s) Text_Mining Factor Type(s) Sample Characteristic(s) Machine-accessible metadata file describing the reported data (ISA-Tab format)

Download PDF Subjects Literature mining Machine learning Abstract Distributed word representations have become an essential foundation for biomedical natural language processing (BioNLP), text mining and information retrieval. Word embeddings are traditionally computed at the word level from a large corpus of unlabeled text, ignoring the information present in the internal structure of words or any information available in domain specific structured resources such as ontologies. However, such information holds potentials for greatly improving the quality of the word representation, as suggeste

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