Italian Journal of Computational Linguistics

Aims & Scope

IJCoL is the half‐yearly journal founded by the Italian Association of Computational Linguistics. The journal intends to cover a broad spectrum of topics related to natural language and computation tackled from different perspectives. IJCoL proposes itself as an updated discussion forum around Computational Linguistics inspired by different perspectives, with the aim of feeding synergies between studies related to different areas of automatic language treatment: from those that share with Computational Linguistics methods and paradigms of analysis, such as computer science and artificial intelligence, or those that share with it the object of study, i.e. languages and language in their different manifestations (such as linguistics, Italian linguistics, sociolinguistics, dialectology, philology), to those amplified by the role of linguistic resources and technologies, for the access and management of their documental heritage. Particular attention will be paid on the one hand to cognitive neuroscience, in which computational modelling has always played a central role, and on the other to the contribution of Computational Linguistics within the wider field of Digital Humanities, which has a long tradition at national level and is now in full-growth. The journal intends therefore to cover a broad spectrum of topics that revolve around language and computation, addressed from different perspectives that include but are not limited to: Automatic treatment of language (written and spoken), Automatic language learning, Computational models of language, cognition and linguistic variation, Acquisition of knowledge from texts, Construction of linguistic resources, Development of infrastructure for interoperability and integration of language resources and technologies, Applications of automatic language processing, such as Information Extraction, Question Answering, automatic summation and machine translation.

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Source: OpenAlex · Each year’s green bar = citations earned by that year’s papers, counted to date — so recent years look lower simply because their papers haven’t had time to be cited yet.

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Subject Classification