NLP Industry Survey Analysis: the industry landscape of natural language use cases in 2020
We recently conducted an industry survey of firms that have natural language systems in production.
This is includes organization which have a history of leveraging NLP systems as well as those which are just beginning to plan their approach.
The “dramatic shift” would be understatement: since 2018, the field of natural language has undergone a sea change.
Breakthroughs in the usage of deep learning, as well as the availability of more sophisticated hardware and cloud resources, led to sudden advances in natural language. The results are pervasive across technology subcategories within the field of natural language: parsing, natural language understanding, sentiment detection, entity linking, speech recognition, abstractive summarization, and so on.
While the tech unicorns and their proxies have conducted almost an “arms race” since early 2018, sometimes publishing papers twice monthly to outdo their competitors’ most recently published benchmarks — how are these advances diffusing into practical use cases, and becoming adopted by mainstream businesses for their needs?
Our survey results explore both the contours of the evolving landscape as well as the industry adoption and business trends for NLP.
Evil Mad Scientist and Founder at Derwen AI
Known as a “player/coach”, with core expertise in data science, natural language, machine learning, cloud computing; 38+ years tech industry experience, ranging from Bell Labs to early-stage start-ups. Co-chair JupyterCon. Advisor for Amplify Partners, IBM Data Science Community, Recognai, KUNGFU.AI, Primer. Lead committer PyTextRank. Formerly: Director, Community Evangelism @ Databricks and Apache Spark. Cited in 2015 as one of the Top 30 People in Big Data and Analytics by Innovation Enterprise.