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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.
Many organizations choose to rely on natural language processing and natural language understanding solutions to address these challenges, but many limit themselves by the approach they take to building their model. Machine learning and symbolic AI have long been considered the only viable approaches to natural language understanding. They have been pitted against each other as mutually exclusive options. This has forced organizations to compromise one way or another.
In a hybrid approach, organizations can use both ML and symbolic in tandem, enabling them to realize the core benefits of each. In this white paper, we examine:
- The traditional approaches to natural language models and the pros and cons of each
- Why symbolic AI and machine learning approaches are not mutually exclusive
- Three unique ways to leverage a hybrid approach for your language model