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expert.ai Natural Language Technologies

Language fuels the enterprise. We find it in everything from emails to videos to business documents and beyond. However, as pervasive as language data is to the enterprise, organizations struggle to maximize its value. Not only is there an incredible amount of language data available and contained within organizations, but an exponentially increasing volume of it, as well.

Organizations need a strategic and repeatable way to leverage the copious amounts of unstructured text data available to them. So, while this momentum towards NLP is a good start, it is just that…a start.

Based on a rich, domain-independent representation of knowledge (knowledge graph), the expert.ai technology platform integrates the best of natural language understanding (NLU) and hybrid AI, the combination of machine learning (ML) and symbolic/rules-based AI, to intelligently automate business processes and harvest relevant knowledge and critical insights from all the information available in your organization.

Here are just a few of the core natural language processing capabilities enabled by expert.ai that customers use to deliver successful and repeatable natural language projects…




Natural Language Understanding

The ability to understand language and extract data in documents, manage interactions in natural language and manage unstructured information is a critical factor for competitive advantage in any industry.

Expert.ai distinguishes out of the box the correct meaning of words and expressions in context and automatically associates the attributes of more general terms that are conceptually linked to these words. This is key to successful natural language understanding. For example, it understands the terms “SUV” and “sedan,” and it automatically comprehends that these words share similar attributes that derive from both being a kind of “car.”

In addition, expert.ai identifies the relationships between concepts in a text. Both features derive from its ability to perform different levels of linguistic analysis (morphological, grammatical and sentence analysis) in conjunction with semantic analysis and word disambiguation. This human-like ability to read text and understand language is a core differentiator from other text analytics platforms because it increases accuracy, speed, and the ability to manage complex text.

Learn more about natural language understanding.

Knowledge Graph & Expansion

Expert.ai’s knowledge graph is a representation of the real world where concepts are defined and connected to one another by semantic relationships. ‘Out of the box’ the expert.ai Knowledge Graph provides both deep and wide coverage of 400k+ English language concepts. Compared to other text analysis technologies that require cumbersome and resource-intensive initial and constant training, expert.ai leverages the embedded knowledge graph together with natural language understanding algorithms to read, comprehend and learn from any text, out-of-the-box.

Unlike other text analytics platforms, expert.ai can add domain-specific, or enterprise-specific, knowledge through both subject matter experts and proprietary machine and deep learning algorithms. Infusing real-world knowledge with disambiguation improves overall natural language model performance.

Learn more about the expert.ai Knowledge Graph.






Hybrid AI – Symbolic + Machine Learning

By leveraging the proprietary symbolic technology described above, expert.ai is uniquely positioned to offer, through our platform a combination of techniques to address the full range of NLP use cases, turning language into knowledge and insight so that teams can make faster, better decisions — the way an expert would. The experience we have gained in implementing real world projects taught us that no single natural language AI technique is a fit for every project. Teams need the flexibility of a hybrid AI approach that integrates symbolic and machine learning (ML) AI techniques to achieve success metrics most valuable to each use case, such as explainability, scalability, and accuracy.

Adopting a Hybrid AI approach provides teams access to NL Models that can perform well in situations where little training data is available. This is often a big consideration when working on domain specific projects such as contract analytics or insurance claims. The hybrid AI approach also provides a more sustainable and ‘greener’ approach to AI computation-heavy ML alone or neural networks.

Learn more about Hybrid AI.

Need your own team of experts? Learn how the experts at expert.ai can help make your natural language project a success.

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