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Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing

Linguists and developers of NLP software have been working separately for many years. Since stochastic methods, such as statistical and neural-network based parsers, have shown to be overwhelmingly successful in the software industry, NLP researchers have typically turned their focus towards technical issues specific to stochastic methods, such as improving recall and precision, and developing larger and larger training corpora. At the same time, linguists kept focusing on problems related to the development of exhaustive and precise resources that are mainly “neutral” vis-a-vis any NLP application, such as parsing and generating sentences. However, recent progress in both fields has been reducing many of these differences, with largecoverage linguistic resources being used more and more by robust NLP software. For instance, NLP researchers now use large dictionaries of multiword units and expressions, and several linguistic experiments have shown the feasibility of using large phrase-structure grammars (a priori used for text parsing) in “generation” mode to automatically produce paraphrases of sentences that are described by these grammars. The First Workshop on Linguistic Resources for Natural Language Processing (LR4NLP) of the 27th International Conference on Computational Linguistics (COLING 2018) held at Santa Fe, New Mexico, August 20, 2018, brought together participants interested in developing large- coverage linguistic resources and researchers with an interest in developing real-world Natural Language Processing (NLP) software. The common theme of all of the papers presented in this workshop was how to build large linguistic resources in the form of annotated corpora, dictionaries, and morphological and syntactic grammars that can be used by NLP applications. Linguists as well as Computational Linguists who work on NLP applications based on linguistic methods will find advanced, up-to-the- minute studies for these themes in this volume. We hope that readers will appreciate the importance of this volume, both for the intrinsic value of each linguistic formalization and the underlying methodology, as well as for the potential for developing automatic NLP applications.

linguistic resources ; NLP ; Monolingual Resources ; Multylingual Resources ; linguistics ; neural nets ; stohastics ; digital collections

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