Punctuation Removal

Introduction

Punctuation removal is the process of removing punctuation marks from text data. Examples of such punctuation marks include periods (.), commas (,), question marks (?), exclamation marks (!), colons (:), semicolons (;), quotation marks (“ ”), parentheses (()), brackets ([]), and hyphens and dashes (-, –, —). Removing such marks produces a text representation that’s less cluttered and more focused on the text’s main ideas, which can improve efforts during data analysis and modeling.

Reasons for punctuation removal

Punctuation removal offers several benefits for various NLP tasks and analyses. A few such benefits include:

• Improved text consistency: Punctuation removal ensures that the text is in a consistent format for analysis. For example, it ensures that different variations of the same word, i.e., “apple” and “apple.” are treated as the same entity, promoting consistency in analysis. The implication is that text analysis models can generate more reliable and consistent results when punctuation is removed.

• Tokenization: Punctuation should be removed for more transparent and precise tokenization. For example, if a sentence is tokenized with punctuation ...