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When working with NLP, there are two key AI techniques that can be used to extract insight and understanding from unstructured language data: symbolic and machine learning. Symbolic is a knowledge-based approach, leveraging knowledge graphs, linguistic feature engines, and reasoning engines. Machine Learning is an inference-based approach using deep learning neural networks, and constructs such as transformers for feature engineering. Both methods have their benefits and challenges. A symbolic approach depends upon the creation of a knowledge graph, while machine learning requires the annotation of large amounts of data. Despite their differences, both approaches complement each other, allowing more to be done with real-world datasets. In this webinar we draw on insights from previous FS Club webinars, discuss both NLP approaches, and provide examples from our research into HybridNL.
Expert.ai has specialised in Natural Language Processing for 20+ years and lists leading global financial services companies amongst its customers. They are the leader in Hybrid NL.
Speaker:
Iain Wadie has been passionate about technology since he was young, he brings experience from research, systems integration and process re-engineering to robotics and automation. He started out working on EU funded research to enable robots to work in abattoirs deconstructing carcasses; this is where he first came across neural networks. He worked in consulting for many years, initially as a developer, then as a programme manager delivering large systems implementation projects, and ultimately as head of a consulting practice for a global software vendor. A role leading the operational control team in group finance for a large UK bank gave him good insight into identifying and delivering the value from digitising business processes. Over the last 5 years Iain has specialised in Cognitive Automation, applying AI to extracting data from the written word. He is pleased to be able to pursue his passion at expert.ai, a company at the forefront of natural language understanding research.
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