Editor
Dr Swayam Prabha Satpathy
Associate Professor, Department of Humanities and Social Sciences, Siksha ‘O’ Anusandhan (Deemed to be University), Odisha, India.
Context
Against the backdrop of the growing interest in the use of artificial language, we invite articles in the field of Language Engineering to explore the scholarly implications of current and future advancements in Natural Language Processing. The issue aims to bridge the gap between theoretical and applied experiments in the fields of computer science and the demands of language adaptations with its concerns and insights for the future.
Since all kinds of communication use natural languages, language technology has become increasingly important in the age of artificial intelligence (AI) and digital communication (DH). The automated study, interpretation, and production of human language, as well as its expansion towards language technology, can greatly benefit from research in the fields of digital humanities and natural language processing. Over four decades, many significant advances have been made due to the multidisciplinary strategy incorporating insights from linguistics, cognitive science, psychology, and machine learning. While widely recognized for its groundbreaking work in the fields of statistical parsing, syntax-based machine translation, and semantic role labeling, digital humanities has also recently led the way in developing methods for few-shot learning applied to NLP tasks, neural model interpretability, and graph neural networks for natural language processing. The development of responsible and socially-oriented NLP technology, together with its applications in media studies, computational social science, and digital humanities, constitutes another recognized field of study. To this end, DH has explored how statistical and neural models can retrieve information from text to help answer questions in the humanities: literature and arts, history, and philosophy, and aid large-scale data-driven analysis of cultural artifacts.
The intervention of humanities in language engineering is crucial because of the creative and constructive role and the addition of the ‘human’ factor in revolutionizing the means of communication. Humanities demand equity, sustainability and language diversity be maintained while moving ahead with new technologies. So, the issue will also consider papers on Language Engineering in other languages. However, the medium of the language needs to be English with proper annotations and translation wherever required.
In this context, the following areas of submission—which are suggestive and not exclusive—will be considered:
- Artificial Intelligence (AI) and Natural Language Processing (NLP): Intersection of AI and NLP, including topics such as deep learning, neural networks, and machine learning algorithms.
- Theoretical Linguistics: Theoretical underpinnings of language, including phonology, morphology, syntax, and semantics.
- Corpus Linguistics: Annotation, abstraction, analysis, and part-of-speech tagging of large corpora of text.
- Machine Translation: Historical developments of machine translation, including rule-based, transfer-based, interlingual, dictionary-based, and neural machine translation, machine translation and signed languages, intellectual property rights, and other related topics.
- Lexicography: Theoretical and practical aspects of lexicography, including the creation of dictionaries, thesauri, and other reference works.
- Text Analysis: Sentiment analysis, record management, and other forms of text analysis.
- Information Retrieval: Historical developments and future possibilities of information retrieval, including search engines, recommender systems, and other related topics.
- System Integration and Language Engineering: Integration of language technology into various systems, including chatbots, virtual assistants, and other human-computer interfaces.
Word limit: 5000-7000 words
Submission Deadline: April 15, 2024.