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Introducing Information Retrieval for Biomedical Informatics Students

2021-05-06 · NAACL (TeachingNLP) 2021 6 · Sanya B. Taneja, Richard D. Boyce, William T. Reynolds, Denis Newman-Griffis

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.

📄 PDF Abstract BibTeX arXiv:2105.02746

Code (1)

dbmi-pitt/bioinf_teachingNLP 공식 구현 tf

Tasks

Information RetrievalRetrieval

Methods 이 논문이 사용한 방법론

Multi-Head Attention 설명 없음
Attention 설명 없음
Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.
Dropout Dropout is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is…
Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…
WordPiece 설명 없음
Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…
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