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Natural Language Processing is a peer-reviewed journal published by Cambridge University Press, covering Natural Language Processing Techniques, Topic Modeling, Hate Speech and Cyberbullying Detection. Over its lifetime it has published 91 papers, which have received 292 citations.
| Publisher | Cambridge University Press |
|---|---|
| Reference style | Numbered Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, A representative article title, Natural Language Processing 12 (2023) 45–58.
Formats any DOI in the closest standard style — Natural Language Processing has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Natural Language Processing Techniques Topic Modeling Hate Speech and Cyberbullying Detection Sentiment Analysis and Opinion Mining Advanced Text Analysis Techniques |
| ISSN | 2977-0424 |
| Citation impact (2-yr) | 3.21 |
| h-index | 8 |
| i10-index | 7 |
| Top institutions publishing here | University of Luxembourg |
| Journal website | www.cambridge.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract This paper addresses the optimization of retrieval-augmented generation (RAG) processes by exploring various methodologies, including advanced RAG methods. The research, driven by the need to enhance RAG processes as highlighted by recent studies, involved a grid-search optimization of 23,625 iterations. We evaluated multiple RAG methods across different vectorstores, embedding models, and large language models,…
Abstract Natural language processing (NLP) has significantly advanced our ability to model and interact with human language through technology. However, these advancements have disproportionately benefited high-resource languages with abundant data for training complex models. Low-resource languages, often spoken by smaller or marginalized communities, need help realizing the full potential of NLP applications. The primary challenges…
Abstract Warning: This paper is based on hate speech detection and may contain examples of abusive/ offensive phrases. Cyberbullying, online harassment, etc., via offensive comments are pervasive across different social media platforms like ™Twitter, ™Facebook, ™YouTube, etc. Hateful comments must be detected and eradicated to prevent harassment and violence on social media. In the Natural…
Abstract The question of context in neural machine translation often focuses on topics related to document-level translation or intersentential context. However, there is a wide range of other aspects that can be considered under the umbrella of context. In this work, we survey ways that researchers have incorporated context into neural machine translation systems and…
Abstract The exponential growth of social media data in the era of Web 2.0 has necessitated advanced techniques for sentiment analysis. While sentiment analysis in monolingual datasets has received significant attention that in code-mixed datasets still need to be studied more. Code-mixed data often contain a mixture of monolingual content (might be in transliterated form),…
