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For the last sixty years and more, computational methods and models have been growing in importance for linguistics, and computational systems for processing human language have attained progressively greater abilities. Research at the meeting point of linguistics and computation, particularly as influenced by work in artificial intelligence and cognitive science, continues to be fruitful in many ways for understanding human language and for developing useful language technologies. Such research work is distributed among many different subfields of linguistics, computer science, psychology, neuroscience, and more.
Language and Computation provides a unified forum for such work. The journal section will promote and publish the highest-quality research at the intersection of language and computation, and which seeks to use information processing models and methods to elucidate the nature, structure, and operation of human language. Furthermore, by being a large tent for diverse related subfields, we aim to enable transformational advances in the understanding of language by facilitating transdisciplinary discussion and collaboration.
Language and Computation welcomes submissions on the full range of research on language and computation, including (but not limited to) work on:
● Computational linguistics
● Models of language and cognition
● Linguistic neuroscience
● Machine learning for language processing
● Knowledge representation
● Cognitive linguistics
● Language evolution
● Language learning
● Child development of language
● Second language learning
● Linguistic information transfer
Position papers describing important issues or trends in research and their implications are of great interest, and we hope to engender meaningful and constructive debate. We specifically encourage perspective articles on current or historical research areas and trends and specifically articles related to the preservation of endangered languages, as well as on language policy in general and on research/professional ethics.
Finally, Language and Computation welcomes submissions responding to and discussing published articles, and will, from time to time, solicit responses to articles of particular significance from diverse relevant researchers. Authors are encouraged to suggest potential respondents to their work, to actively encourage discussion.
Replication and Availability of Resources
Science is an inherently cumulative activity, hence it is essential that others should be able to replicate and build upon published work. Authors are therefore strongly encouraged to make data, code, surveys, protocols, and other resources needed for replicating research published in Language and Computation promptly available to readers without undue restrictions. Any restrictions on such availability of materials must be disclosed at the time of submission. Ideally, any such restrictions must also be discussed in the submitted manuscript.
By the same token, Language and Computation explicitly solicits and welcomes replication reports of previously-published research, as Empirical Studies. Replications should be clearly labeled as such upon submission, and will be reviewed for fidelity to the original study as well as the usual criteria of methodological soundness and completeness. Responses will be solicited from the original researchers upon acceptance of any replication study for publication.
Indexed in: Google Scholar, DOAJ, CrossRef, CLOCKSS, OpenAIRE
Language and Computation welcomes submissions of the following article types: Brief Research Report, Conceptual Analysis, Core Concept, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, New Discovery, Opinion, Original Research, Perspective, Policy and Practice Reviews, Review, Specialty Grand Challenge and Technology and Code.
All manuscripts must be submitted directly to the section Language and Computation, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Language and Computation will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Artificial Intelligence.
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