Hybrid Natural Language Processing: An Introduction

Abstract

The proliferation of knowledge graphs and recent advances in artificial intelligence have raised great expectations related to the combination of symbolic and data-driven approaches in cognitive tasks. This is particularly the case of knowledge-based approaches to natural language processing as near-human symbolic understanding relies on expressive, structured knowledge representations. Engineered by humans, knowledge graphs are frequently well curated and of high quality, but they can also be labor-intensive, rely on rigid formalisms and sometimes be biased towards the specific viewpoint of their authors. This book aims to provide the reader with means to address limitations like the above by bringing together bottom-up, data-driven models and top-down, structured knowledge graphs. To this purpose, the book explores how to reconcile both views and enrich the resulting representations beyond the possibilities of each individual approach. Throughout this book, we delve into this idea and show how such hybrid approach can be used with great effectiveness in a variety of natural language processing tasks.