If you are dealing with natural language—whether in product reviews, user feedback, or interaction with customers—you are likely to benefit from the latest advances in natural language understanding. With the deep learning revolution, AI is no longer the privilege of large corporates; most businesses that deal with language data can apply NLU techniques in their existing solutions or create new services.
NLU algorithms are available in many open source tools, including machine-learning and deep learning toolkits or the word2vec package. And of course there are many commercially available APIs. But what are their advantages and limitations? When can you build your own solution, and when is it better to buy? Alyona Medelyan surveys the newest tools for dealing with language, showcases some common business use cases, and provides insight into what’s brewing in academic research and what we can expect in the near future.
Alyona Medelyan has been working on algorithms that make sense of language data for over a decade. Her passion lies in helping businesses to extracting useful knowledge from text. As part of her PhD she has proven that her open source algorithm, Maui, can be as accurate as people at finding keywords. She has worked with large multinationals like Cisco and Google, has lead R&D teams and consulted to small and large companies around the globe. Alyona now runs Thematic, a customer insight company.
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