depun

Depun is a term used in information technology to denote the process of removing punctuation from text data. It is often described as short for "depunctuation" and is used in informal writing and some software documentation to describe a preprocessing step in natural language processing pipelines. The term does not have a single, formal definition and is not part of a universal standard.

In practice, depun involves stripping punctuation characters, and may also include related normalization steps such as converting to lowercase, removing diacritics, and collapsing whitespace. The exact scope can vary by project: some implementations remove only common ASCII punctuation, while others handle language-specific marks, symbols, or commas within numbers. Because punctuation can carry meaning in certain tasks (for example, indicating sentence boundaries or separating numeric values), the decision to apply depun is task-dependent.

Variants and related concepts include punctuation removal, text normalization, and tokenization. Some NLP pipelines treat depun as one stage in a broader preprocessing sequence, while others preserve punctuation for downstream analysis. The absence or presence of punctuation can impact downstream tasks such as sentiment analysis, named-entity recognition, and parsing, so practitioners tailor depun to the goals of their model and data.

Overall, depun is a practical convenience term used to describe a common preprocessing choice in text analytics. It is one of several tools in the broader field of text normalization and data cleaning, and its usage reflects the needs of specific applications.