During the recent years, the use of linguistic data for language processing increased progressively. Such data are now commonly called language resources. Most of the language resources used for this purpose are collections of texts as the Brown Corpus and the Penn Treebank, but electronic lexicons (WordNet, FrameNet, VerbNet, ComLex, Lexicon-Grammar...) and formal grammars (TAG...) developed recently. Most processes of construction of lexicons and grammars are manual, whereas the construction of corpora has always been highly automated. However, more and more specialists of language processing realize that the information content of lexicons and grammars is richer than that of corpora, and hence the former make more elaborate processing possible. The difference in construction time is likely to be connected with the difference in information content: the handcrafting of lexicons and grammars by linguists would make them more informative than automatically generated data. This situation can evolve into two directions: either specialists of language technology get progressively used to handling manually constructed resources, which are more informative and more complex, or the process of construction of lexicons and grammars is automated and industrialized, which is the mainstream perspective. Both evolutions are already in progress, and a tension exists between them. The relation between linguists and computer scientists depends on the future of these evolutions, since the first implies training and hiring numerous linguists, whereas the other depends essentially on solutions elaborated by computer engineers. The aim of this article is to analyse practical examples of the language resources in question, and to discuss about which of the two trends, handcrafting or generating industrially, or a combination of both, can give the best results or is the most realistic.
This paper reflects on twenty years of building NLP resources and research infrastructure for Arabic, a language spoken by hundreds of millions yet historically underserved relative to languages such as English or Chinese. The first decade focused on foundational linguistic infrastructure; the second shifted toward computational social science, social media analysis, and socially oriented applications. Rather than cataloguing outputs, the paper examines what the experience of building them revealed. Three counterintuitive lessons emerge: building datasets is as much a social process as a technical one; communities formed around shared tasks often matter more than the tasks themselves; and moving from language resources to computational social science exposes challenges that traditional NLP training does not address. We discuss three failures: a depression detection corpus that never reached clinical practice, a period of spreading across too many shared tasks without sufficient depth, and a long-standing assumption that Modern Standard Arabic infrastructure would transfer cleanly to dialectal tasks. These experiences suggest that the hardest problems in developing NLP for underserved communities are not linguistic but social, institutional, and epistemic, and require competencies the field rarely teaches.