Practical Natural Language Processing

Foreword

The field of natural language processing (NLP) has undergone a dramatic shift in recent years, both in terms of methodology and in terms of the applications supported. Methodological advances have ranged from new ways of representing documents to new techniques for language synthesis. With these have come new applications ranging from open-ended conversational systems to techniques that use natural language for model interpretability. Finally, these advances have seen NLP gain a foothold in related areas, such as computer vision and recommender systems, some of which my lab is working on with support from Amazon, Samsung, and the National Science Foundation.

As NLP is expanding into these exciting new areas, so too has the audience of practitioners wanting to make use of NLP techniques. In the Data Science course (CSE 258) that I take at the University of California–San Diego, which is often the most attended in the computer science department, I see that more and more students are doing their projects on NLP-based topics. NLP is rapidly becoming a necessary skill required by engineers, product managers, scientists, students, and enthusiasts wishing to build applications on top of natural language data. On one hand, new tools and libraries for NLP and machine learning have made natural language modeling more accessible than ever. But on the other hand, resources for learning NLP must target this ever-growing and diverse audience. This is especially true for organizations ...