  Natural language processing (NLP) researchers develop models of grammar,
meaning and communication based on written text. Due to task and data
differences, what is considered text can vary substantially across studies. A
conceptual framework for systematically capturing these differences is lacking.
We argue that clarity on the notion of text is crucial for reproducible and
generalizable NLP. Towards that goal, we propose common terminology to discuss
the production and transformation of textual data, and introduce a two-tier
taxonomy of linguistic and non-linguistic elements that are available in
textual sources and can be used in NLP modeling. We apply this taxonomy to
survey existing work that extends the notion of text beyond the conservative
language-centered view. We outline key desiderata and challenges of the
emerging inclusive approach to text in NLP, and suggest community-level
reporting as a crucial next step to consolidate the discussion.
