Automatic Processing Of Natural Language Electronic Texts With Nooj
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Automatic Processing of Natural-Language Electronic Texts with NooJ
This book constitutes the refereed proceedings of the 9th International Conference, NooJ 2015, held in Minsk, Belarus, in June 2015. NooJ 2015 received 51 submissions. The 20 revised full papers presented in this volume were carefully reviewed and selected from the 35 papers that were presented at the conference. The papers are organized in topical sections on corpora, vocabulary and morphology; syntax and semantics; application.
Automatic Processing of Natural-Language Electronic Texts with NooJ
This book constitutes the refereed proceedings of the 10th International Conference, NooJ 2016, held České Budějovice, Czech Republic, in June 2016. The 21 revised full papers presented in this volume were carefully reviewed and selected from 45 submissions. NooJ is a linguistic development environment that provides tools for linguists to construct linguistic resources that formalise a large gamut of linguistic phenomena: typography, orthography, lexicons for simple words, multiword units and discontinuous expressions, inflectional and derivational morphology, local, structural and transformational syntax, and semantics.
Linguistic Resources for Natural Language Processing
Empirical — data-driven, neural network-based, probabilistic, and statistical — 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. In consequence, 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 extract solutions from massive databases used as cheat sheets. In the same manner, Natural Language Processing (NLP) software that uses training corpora associated with empirical methods are trendy, as most researchers in NLP today use large training corpora, always to the detriment of the development of formalized dictionaries and grammars. Not questioning the intrinsic value of many software applications based on empirical 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 could be successfully used to enhance current NLP software applications. The second part presents two representative cases where data-driven approaches cannot be implemented simply because there is not enough data available for low-resource languages. The third part addresses the problem of how to treat multiword units in NLP software, which is arguably the weakest point of NLP applications today but has a simple and elegant linguistic solution. 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.