Abstract
Synonymous relationships among biomedical terms are extensively annotated within specialized terminologies, implying that synonymy is important for practical computational applications within this field. It remains unclear, however, whether text mining actually benefits from documented synonymy and whether existing biomedical thesauri provide adequate coverage of these linguistic relationships. In this study, we examine the impact and extent of undocumented synonymy within a very large compendium of biomedical thesauri. First, we demonstrate that missing synonymy has a significant negative impact on named entity normalization, an important problem within the field of biomedical text mining. To estimate the amount synonymy currently missing from thesauri, we develop a probabilistic model for the construction of synonym terminologies that is capable of handling a wide range of potential biases, and we evaluate its performance using the broader domain of near-synonymy among general English words. Our model predicts that over 90% of these relationships are currently undocumented, a result that we support experimentally through “crowd-sourcing.” Finally, we apply our model to biomedical terminologies and predict that they are missing the vast majority (>90%) of the synonymous relationships they intend to document. Overall, our results expose the dramatic incompleteness of current biomedical thesauri and suggest the need for “next-generation,” high-coverage lexical terminologies.
Highlights
Most words and phrases in English possess synonyms— expressions that share close or identical meaning within a restricted cultural context [1]
We establish the importance of synonymy for a specific text-mining task, and we suggest that current thesauri may be woefully inadequate in their documentation of this linguistic phenomenon
We apply our model to both biomedical terminologies and general-English thesauri, predicting massive amounts of missing synonymy for both lexicons
Summary
Most words and phrases in English possess synonyms— expressions that share close or identical meaning within a restricted cultural context [1]. Synonyms can be seen as a simple, concise way of encoding the semantics of individual words [3], which makes them useful for artificial intelligence applications. Much like their human counterparts, computer programs that parse natural language must rely on a finite set of ‘‘known’’ synonymous relationships, so deficiencies in their thesauri could have a profound impact on their ability to process human communication. Synonymy is extensively documented within many large computational lexicons [4,5], but it is not immediately obvious whether inclusion of synonyms sufficiently improves natural language processing results to justify the computational overhead
Sign-in/Register to view highlights & summary 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.
