  Background & Objective: Biomedical text data are increasingly available for
research. Tokenization is an initial step in many biomedical text mining
pipelines. Tokenization is the process of parsing an input biomedical sentence
(represented as a digital character sequence) into a discrete set of word/token
symbols, which convey focused semantic/syntactic meaning. The objective of this
study is to explore variation in tokenizer outputs when applied across a series
of challenging biomedical sentences.
  Method: Diaz [2015] introduce 24 challenging example biomedical sentences for
comparing tokenizer performance. In this study, we descriptively explore
variation in outputs of eight tokenizers applied to each example biomedical
sentence. The tokenizers compared in this study are the NLTK white space
tokenizer, the NLTK Penn Tree Bank tokenizer, Spacy and SciSpacy tokenizers,
Stanza/Stanza-Craft tokenizers, the UDPipe tokenizer, and R-tokenizers.
  Results: For many examples, tokenizers performed similarly effectively;
however, for certain examples, there were meaningful variation in returned
outputs. The white space tokenizer often performed differently than other
tokenizers. We observed performance similarities for tokenizers implementing
rule-based systems (e.g. pattern matching and regular expressions) and
tokenizers implementing neural architectures for token classification.
Oftentimes, the challenging tokens resulting in the greatest variation in
outputs, are those words which convey substantive and focused
biomedical/clinical meaning (e.g. x-ray, IL-10, TCR/CD3, CD4+ CD8+, and
(Ca2+)-regulated).
  Conclusion: When state-of-the-art, open-source tokenizers from Python and R
were applied to a series of challenging biomedical example sentences, we
observed subtle variation in the returned outputs.
