Concept recognition as a machine translation problem.

Background: Automated assignment of specific ontology concepts to mentions in text is a critical task in biomedical natural language processing, and the subject of many open shared tasks. Although the current state of the art involves the use of neural network language models as a post-processing step, the very large number of ontology classes to be recognized and the limited amount of gold-standard training data has impeded the creation of end-to-end systems based entirely on machine learning. Recently, Hailu et al.

Ontology translation: A case study on translating the Gene Ontology from English to German.

For many researchers, the purpose of ontologies is sharing data. This sharing is facilitated when ontologies are available in multiple languages, but inhibited when an ontology is only available in a single language. Ontologies should be accessible to people in multiple languages, since multilingualism is inevitable in any scientific work.

Sublanguage Corpus Analysis Toolkit: A tool for assessing the representativeness and sublanguage characteristics of corpora.

Sublanguages are varieties of language that form "subsets" of the general language, typically exhibiting particular types of lexical, semantic, and other restrictions and deviance. SubCAT, the Sublanguage Corpus Analysis Toolkit, assesses the representativeness and closure properties of corpora to analyze the extent to which they are either sublanguages, or representative samples of the general language. The current version of SubCAT contains scripts and applications for assessing lexical closure, morphological closure, sentence type closure, over-represented words, and syntactic deviance.