An Analysis of Biomedical Tokenization: Problems and Strategies
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingNamed Entity Recognition (NER)Similar Papers 제목 키워드 기반
How Important Is Tokenization in French Medical Masked Language Models?
Subword tokenization has become the prevailing standard in the field of natural language processing (NLP) over recent years, primarily due to the widespread utilization of pre-trained language models. This shift began wi…
An Information Extraction Study: Take In Mind the Tokenization!
Current research on the advantages and trade-offs of using characters, instead of tokenized text, as input for deep learning models, has evolved substantially. New token-free models remove the traditional tokenization st…
Inductive BiasNamed Entity Recognition (NER)Relation ExtractionEnd-to-End Models for Chemical-Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies
End-to-end relation extraction (E2ERE) is an important task in information extraction, more so for biomedicine as scientific literature continues to grow exponentially. E2ERE typically involves identifying entities (or n…
Chemical-Protein Interaction Extractionnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+4Biomedical Language Models are Robust to Sub-optimal Tokenization
As opposed to general English, many concepts in biomedical terminology have been designed in recent history by biomedical professionals with the goal of being precise and concise. This is often achieved by concatenating …
Entity LinkingLanguage ModelingLanguage ModellingMasked Language Modeling+4Evaluating Interval-based Tokenization for Pitch Representation in Symbolic Music Analysis
Symbolic music analysis tasks are often performed by models originally developed for Natural Language Processing, such as Transformers. Such models require the input data to be represented as sequences, which is achieved…