Introducing Information Retrieval for Biomedical Informatics Students
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Abstract (arXiv)
Introducing biomedical informatics (BMI) students to natural language processing (NLP) requires balancing technical depth with practical know-how to address application-focused needs. We developed a set of three activities introducing introductory BMI students to information retrieval with NLP, covering document representation strategies and language models from TF-IDF to BERT. These activities provide students with hands-on experience targeted towards common use cases, and introduce fundamental components of NLP workflows for a wide variety of applications.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 13 references that are in the map. First posted on arXiv 6 May 2021.
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8 of 13 references.
References · 8
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
- 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
- 2019Publicly Available Clinical BERT Embeddingscited by 1,002
- 2018Lessons from Natural Language Inference in the Clinical Domaincited by 151
- 2021Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?cited by 93
- 2018Pytrec_eval: An Extremely Fast Python Interface to trec_evalcited by 46
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