The impact of learning Unified Medical Language System knowledge embeddings in relation extraction from biomedical texts.

ABSTRACT:

Objective

We explored how knowledge embeddings (KEs) learned from the Unified Medical Language System (UMLS) Metathesaurus impact the quality of relation extraction on 2 diverse sets of biomedical texts.

Materials and methods

Two forms of KEs were learned for concepts and relation types from the UMLS Metathesaurus, namely lexicalized knowledge embeddings (LKEs) and unlexicalized KEs. A knowledge embedding encoder (KEE) enabled learning either LKEs or unlexicalized KEs as well as neural models capable of producing LKEs for mentions of biomedical concepts in texts and relation types that are not encoded in the UMLS Metathesaurus. This allowed us to design the relation extraction with knowledge embeddings (REKE) system, which incorporates either LKEs or unlexicalized KEs produc

SUBMITTER: Weinzierl MA 

PROVIDER: S-EPMC7647370 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature