Natural Language Processing (NLP) and Large Language Modelling

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: 30 November 2024 | Viewed by 510

Special Issue Editor


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Guest Editor
School of Info Technology, Faculty of Science, Engineering and Built Environment, Geelong Waurn Ponds Campus, Deakin University, Geelong, VIC 3216, Australia
Interests: natural language processing; small efficient language modelling; continual learning; text generation; adversarial learning; scientific text mining; multimodality; conversational systems

Special Issue Information

Dear Colleagues,

NLP is a rapidly evolving field that plays a crucial role in sha** the future of human–computer interactions, with applications ranging from sentiment analysis and machine translation to question answering and dialogue systems.

We invite researchers, practitioners, and enthusiasts to submit original research articles, reviews, and case studies that contribute to the advancement of NLP. Extended conference papers are also welcome, but they should contain at least 50% of new material, e.g., in the form of technical extensions, more in-depth evaluations, or additional use cases. Topics of interest for this Special Issue include, but are not limited to, the following:

  • Large language modelling and its applications;
  • Sentiment analysis and opinion mining;
  • Machine translation and multilingual processing;
  • Question answering and information retrieval;
  • Dialogue systems and conversational agents;
  • Text summarization and generation;
  • Natural language understanding and generation;
  • NLP applications in healthcare, finance, education, and other domains.

Submissions should present novel research findings, innovative methodologies, and practical applications that demonstrate the current state of the art in NLP. We welcome interdisciplinary approaches and encourage submissions that explore the intersection of NLP with other fields, such as machine learning, artificial intelligence, and cognitive science.

Dr. Ming Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at mdpi.longhoe.net by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • natural language processing
  • small efficient language modelling
  • continual learning
  • text generation
  • adversarial learning
  • scientific text mining
  • multimodality
  • conversational systems
  • large language model

Published Papers (1 paper)

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Research

24 pages, 501 KiB  
Article
An NLP-Based Exploration of Variance in Student Writing and Syntax: Implications for Automated Writing Evaluation
by Maria Goldshtein, Amin G. Alhashim and Rod D. Roscoe
Computers 2024, 13(7), 160; https://doi.org/10.3390/computers13070160 - 25 Jun 2024
Viewed by 265
Abstract
In writing assessment, expert human evaluators ideally judge individual essays with attention to variance among writers’ syntactic patterns. There are many ways to compose text successfully or less successfully. For automated writing evaluation (AWE) systems to provide accurate assessment and relevant feedback, they [...] Read more.
In writing assessment, expert human evaluators ideally judge individual essays with attention to variance among writers’ syntactic patterns. There are many ways to compose text successfully or less successfully. For automated writing evaluation (AWE) systems to provide accurate assessment and relevant feedback, they must be able to consider similar kinds of variance. The current study employed natural language processing (NLP) to explore variance in syntactic complexity and sophistication across clusters characterized in a large corpus (n = 36,207) of middle school and high school argumentative essays. Using NLP tools, k-means clustering, and discriminant function analysis (DFA), we observed that student writers employed four distinct syntactic patterns: (1) familiar and descriptive language, (2) consistently simple noun phrases, (3) variably complex noun phrases, and (4) moderate complexity with less familiar language. Importantly, each pattern spanned the full range of writing quality; there were no syntactic patterns consistently evaluated as “good” or “bad”. These findings support the need for nuanced approaches in automated writing assessment while informing ways that AWE can participate in that process. Future AWE research can and should explore similar variability across other detectable elements of writing (e.g., vocabulary, cohesion, discursive cues, and sentiment) via diverse modeling methods. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) and Large Language Modelling)
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