Best Natural Language Processing Software

Compare the Top Natural Language Processing Software as of February 2026

What is Natural Language Processing Software?

Natural language processing (NLP) software analyzes both written and spoken human languages and interprets them for translation, deep learning and automation purposes. Natural language processing software may also include natural language understanding (NLU) capabilities. Compare and read user reviews of the best Natural Language Processing software currently available using the table below. This list is updated regularly.

  • 1
    ResoluteAI

    ResoluteAI

    ResoluteAI

    ResoluteAI's secure platform lets you search aggregated scientific, regulatory, and business databases simultaneously. Combined with our interactive analytics and downloadable visualizations, you can make connections that lead to breakthrough discoveries. Nebula is ResoluteAI's enterprise search product for science. We apply structured metadata and a range of AI capabilities to your institutional knowledge. This includes NLP, OCR, image recognition, and transcription, making your proprietary information easily findable and accessible. With Nebula, you have the power to unlock the hidden value in your research, experiments, market intelligence, and acquired assets. Structured metadata created from unstructured text, semantic expansion, conceptual search, and document similarity search.
  • 2
    Iris.ai

    Iris.ai

    Iris.ai

    Iris.ai is a world-leading and award-winning AI engine for scientific text understanding. It is a comprehensive platform for all research-related knowledge processing needs. Our Researcher Workspace solution provides smart search and a wide range of smart filters, reading list analysis, auto-generated summaries, autonomous extraction, and systematising of data. Iris.ai allows humans to focus on value creation by saving 75% of a researcher’s time, doing specialised, interdisciplinary field analysis to an above human level of accuracy. Its algorithms for text similarity, tabular data extraction, domain-specific entity representation learning, and entity disambiguation and linking measure up to the best in the world. Its machine builds a comprehensive knowledge graph containing all entities and their linkages to allow humans to learn from it, use it, and give feedback to the system. Applying these features to scientific and technical text is a complicated challenge few others can achieve.
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