Applications of natural language understanding: Tools and technologies

With the rise of deep learning, natural language understanding techniques are becoming more effective and are not as reliant on costly annotated data. This leads to an explosion of possibilities of what businesses can do with language. Alyona Medelyan explains what the newest NLU tools can achieve today and presents their common use cases.

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.