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MESS: A Multilingual Error based String Similarity measure for transliterated name variants.

, , , and . FIRE, page 47-50. ACM, (2015)

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A Survey on Context Learning., , , and . IEEE Trans. Knowl. Data Eng., 29 (1): 38-56 (2017)A survey on literature based discovery approaches in biomedical domain., , , and . J. Biomed. Informatics, (2019)Augmenting word embeddings through external knowledge-base for biomedical application., , , and . BigData, page 1965-1974. IEEE Computer Society, (2017)Topic Discovery for Short Texts Using Word Embeddings., , , , , and . ICDM, page 1299-1304. IEEE Computer Society, (2016)Data-driven Risk Characterization and Prediction of Renal Failure among Diabetic Type 2 Patients using Electronic Medical Records., , , and . AMIA, AMIA, (2017)DWE-Med: Dynamic Word Embeddings for Medical Domain., , , and . ACM Trans. Knowl. Discov. Data, 13 (2): 19:1-19:21 (2019)A Novel Data-Driven Framework for Risk Characterization and Prediction from Electronic Medical Records: A Case Study of Renal Failure., , , and . CoRR, (2017)Concepts-Bridges: Uncovering Conceptual Bridges Based on Biomedical Concept Evolution., , , , and . KDD, page 1599-1607. ACM, (2018)Matching product titles using web-based enrichment., , , , and . CIKM, page 605-614. ACM, (2012)Comparison of Drug Classifications for Antidiabetic Agents in Real-World Data., , , and . AMIA, AMIA, (2018)