A JavaScript library for parsing text to extract dates, times, phone numbers, emails, places, and other structured information.
Knwl.js is a JavaScript library that parses unstructured text to extract structured information like dates, times, phone numbers, emails, and places. It solves the problem of manually scanning and processing text for specific data points by automating detection and extraction. The library uses a modular plugin system to support various parsing tasks and languages.
Developers building applications that need to process and analyze text data, such as chatbots, content management systems, or data mining tools. It's particularly useful for those working with user-generated content, logs, or documents where structured information is embedded in free text.
Developers choose Knwl.js for its simplicity, extensibility, and lightweight design. Unlike heavier NLP solutions, it focuses on specific, common parsing tasks with an easy-to-use plugin system, making it ideal for quick integration and customization without complex dependencies.
Find Dates, Places, Times, and More. A .js library for parsing text for specific information.
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The plugin-based architecture allows developers to add custom parsers, as documented in plugin_development.md, making it adaptable to new data types and languages without overhauling the core library.
Works in both Node.js and browser environments, with clear installation instructions for each, enabling deployment in diverse applications from servers to client-side web apps.
Includes ready-to-use plugins for dates, times, phones, emails, and links, simplifying integration for everyday parsing tasks without additional configuration or dependencies.
Plugins like english.js are labeled as experimental, rigid, and simplistic in the README, leading to unreliable results for complex parsing such as sentence structure analysis.
The README explicitly links to a GitHub issue discussing the project's future, indicating potential stagnation, lack of updates, and limited long-term support for users.
Primarily focused on English and basic data types; lacks advanced NLP capabilities like sentiment analysis or robust multi-language parsing, requiring custom plugin development for expansion.