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The clinical named entity recognition (CNER) task seeks to locate andclassify clinical terminologies into predefined categories, such as diagnosticprocedure, disease disorder, severity, medication, medication dosage, and signsymptom. CNER facilitates the study of side-effect on medications includingidentification of novel phenomena and human-focused information extraction.Existing approaches in extracting the entities of interests focus on usingstatic word embeddings to represent each word. However, one word can havedifferent interpretations that depend on the context of the sentences.Evidently, static word embeddings are insufficient to integrate the diverseinterpretation of a word. To overcome this challenge, the technique ofcontextualized word embedding has been introduced to better capture thesemantic meaning of each word based on its context. Two of these languagemodels, ELMo and Flair, have been widely used in the field of Natural LanguageProcessing to generate the contextualized word embeddings on domain-genericdocuments. However, these embeddings are usually too general to capture theproximity among vocabularies of specific domains. To facilitate variousdownstream applications using clinical case reports (CCRs), we pre-train twodeep contextualized language models, Clinical Embeddings from Language Model(C-ELMo) and Clinical Contextual String Embeddings (C-Flair) using theclinical-related corpus from the PubMed Central. Explicit experiments show thatour models gain dramatic improvements compared to both static word embeddingsand domain-generic language models.

Clinical Named Entity Recognition using Contextualized Token Representations