Abstract
Morphological analysis as applied to English has generally involved the study of rules for inflections and derivations. Recent work has attempted to derive such rules from automatic analysis of corpora. Here we study similar issues, but in the context of the biological literature. We introduce a new approach which allows us to assign probabilities of the semantic relatedness of pairs of tokens that occur in text in consequence of their relatedness as character strings. Our analysis is based on over 84 million sentences that compose the MEDLINE database and over 2.3 million token types that occur in MEDLINE and enables us to identify over 36 million token type pairs which have assigned probabilities of semantic relatedness of at least 0.7 based on their similarity as strings.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
