Linguistic Resources for Natural Language Processing

Stochastic methods seem to be the modern trend. Recently, OpenAI's ChatGPT, Google's Bard and Microsoft's Sydney chatbots have been garnering a lot of attention for their detailed answers across many knowledge domains. Most AI researchers are no longer interested in trying to understand what common intelligence is or how intelligent agents construct scenarios to solve various problems. Instead, they now develop systems that use stochastic methods to extract solutions from massive databases used as cheat sheets. In the same manner, Natural Language Processing (NLP) software that uses training corpora associated with stochastic methods are being used daily. More generally, researchers use the spectacular results produced by statistical- or neuron-network-based NLP applications to validate the massive use of training corpora, always to the detriment of the development of linguistic resources.Not questioning the intrinsic value of many software applications based on stochastic methods, this volume aims at rehabilitating the linguistic approach to NLP. In an introduction, the editor uncovers several limitations and flaws of using training corpora to develop NLP applications, even the simplest ones, such as automatic taggers. The first part of the volume is dedicated to showing how carefully handcrafted linguistic resources can be successfully used to enhance NLP software applications. The second part presents two representative cases where data-driven approaches cannot be implemented because there is not enough data available: low-resource languages. The third part addresses the problem of how to treat multiword units in NLP software.It is the editor's belief that readers interested in Natural Language Processing will appreciate the importance of this volume, both for its questioning of the training corpus-based approaches and for the intrinsic value of the linguistic formalization and the underlying methodology presented.