tonormalize

tonormalize is a Python library designed to simplify the process of normalizing text data for natural language processing (NLP) tasks. It provides a collection of preprocessing utilities that handle common text normalization challenges, such as case conversion, punctuation removal, tokenization, and lemmatization. The library is particularly useful for researchers and developers working with unstructured text data, as it automates repetitive preprocessing steps, ensuring consistency and efficiency in text preparation.

The library supports multiple languages and offers customizable options for each normalization step. For example, users can choose between different tokenization strategies, such as word-level or character-level splitting, and apply language-specific lemmatization or stemming techniques. Additionally, tonormalize includes utilities for handling special characters, numbers, and domain-specific terminology, making it adaptable to various use cases, including sentiment analysis, topic modeling, and machine translation.

One of the key features of tonormalize is its modular design, allowing users to select only the preprocessing steps relevant to their project. This flexibility reduces computational overhead and makes the library suitable for both small-scale experiments and large-scale deployments. The library also integrates with popular NLP frameworks like spaCy and NLTK, enabling seamless incorporation into existing pipelines.

tonormalize is open-source and maintained by a community of contributors, ensuring ongoing improvements and compatibility with emerging NLP tools. It is distributed under an open license, making it freely available for academic and commercial use. While primarily focused on text normalization, the library can serve as a foundational component in broader NLP workflows, helping to standardize input data for improved model performance and reliability.