Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
New Developments in Computational Linguistics to Support Decision Making
share announcement

New Developments in Computational Linguistics to Support Decision Making

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2024 | Viewed by 1188

Special Issue Editors

Dr. Marco Palomino

E-Mail Website
Guest Editor
School of Natural and Computing Sciences, King's College, University of Aberdeen, Aberdeen AB24 3FX, Scotland, UK
Interests: sentiment analysis; natural language processing; horizon scanning; web crawling; web searching
Dr. Craig McNeile

E-Mail Website
Guest Editor
School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK
Interests: natural language processing

Special Issue Information

Dear Colleagues,

Many interdisciplinary applications benefit from comprehending and analyzing written and spoken language. If computers recognize and understand what we write or speak, our interaction with software and machines can improve, which, in turn, enhances our ability to use the information available for strategic insights and decision making.

At present, modelling linguistic phenomena computationally relies on a variety of tools, including machine learning, deep learning, cognitive computing, neuroscience, and language analysis. This Special Issue on ‘New Developments in Computational Linguistics to Support Decision Making’ aims to bring together the latest research and innovations in computational linguistic tools to address the challenges of supporting decision making in several fields, such as medical diagnostics, customer service, consumer behavior prediction, production optimization, asset allocation, etc.

We invite authors to submit high-quality original research papers focusing on, but not limited to, the following topics:

  • Finite state techniques;
  • N-gram language models;
  • Sentiment classification;
  • Sequence labelling for part of speech and named entities;
  • Constituency grammars and treebanks;
  • Constituency parsing;
  • Compositional semantics;
  • Distributional semantics;
  • Neural networks and neural language models;
  • Word senses and WordNet;
  • Computational discourse;
  • Dialogue systems and chatbots;
  • Large language models;
  • Information extraction and question answering;
  • Machine translation.

Dr. Marco Palomino
Dr. Craig McNeile
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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
  • computational linguistics
  • machine learning
  • language models
  • text mining

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 1847 KiB  
Article
Topic Modelling of Management Research Assertions to Develop Insights into the Role of Artificial Intelligence in Enhancing the Value Propositions of Early-Stage Growth-Oriented Companies
by Stoyan Tanev, Christian Keen, Tony Bailetti and David Hudson
Appl. Sci. 2024, 14(8), 3277; https://doi.org/10.3390/app14083277 - 13 Apr 2024
Viewed by 304
Abstract
The article suggests a Value Proposition (VP) framework that enables analysis of the beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP activities. To develop such a framework, we examined existing business and management research publications to identify and extract [...] Read more.
The article suggests a Value Proposition (VP) framework that enables analysis of the beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP activities. To develop such a framework, we examined existing business and management research publications to identify and extract assertions that could be used as a source of actionable insights for early-stage growth-oriented companies. The extracted assertions were assembled into a corpus of texts that was subjected to topic modelling analysis—a machine learning approach to natural language processing that is used to identify latent themes in large corpora of text documents. The topic modelling resulted in the identification of seven topics. Each topic is defined by a set of most frequent words co-occurring in a distinctive subset of texts that could be interpreted in terms of activities constituting the core elements of the VP framework. We then examined each activity in terms of its potential to be enhanced by employing AI resources and capabilities. The interpretation of the topic modelling results led to the identification of seven topics: (1) Value created; (2) Stakeholder value propositions; (3) Foreign market entry; (4) Customer base; (5) Continuous improvement; (6) Cross-border operations; and (7) Company image. The uniqueness of the adopted topic modelling approach consists in the quality of the assertions and the interpretation of the seven topics as an activity framework, i.e., in its capacity to generate actionable insights for practitioners. The additional analysis suggests that there is a potential for AI to enhance the emerging four core elements of the VP framework: Value created, Stakeholder value propositions, Foreign market entry, and Customer base. More importantly, we found that the greatest number of assertions related to activities that could be enhanced by AI are part of the Customer base topic, i.e., the topic that is most directly related to the growth potential of the companies. In addition, the VP framework suggests that a company’s customer base growth is continuously enhanced through a positive loop enabled by activities focused on the Continuous improvement of the activities and the amount of Value created, the alignment of Stakeholder value propositions, and companies’ Foreign market entry. Thus, the multiple-stakeholder perspective on VP development and foreign market entry appears as a factor that helps in understanding the beneficial impact of AI on the enhancement of the VP of early-stage growth-oriented companies. Full article
(This article belongs to the Special Issue New Developments in Computational Linguistics to Support Decision Making)
►▼ Show Figures

Figure 1

17 pages, 2581 KiB  
Article
Automated Scoring of Translations with BERT Models: Chinese and English Language Case Study
by Yizhuo Cui and Maocheng Liang
Appl. Sci. 2024, 14(5), 1925; https://doi.org/10.3390/app14051925 - 26 Feb 2024
Viewed by 580
Abstract
With the wide application of artificial intelligence represented by deep learning in natural language-processing tasks, the automated scoring of translations has also advanced and improved. This study aims to determine if the BERT-assist system can reliably assess translation quality and identify high-quality translations [...] Read more.
With the wide application of artificial intelligence represented by deep learning in natural language-processing tasks, the automated scoring of translations has also advanced and improved. This study aims to determine if the BERT-assist system can reliably assess translation quality and identify high-quality translations for potential recognition. It takes the Han Suyin International Translation Contest as a case study, which is a large-scale and influential translation contest in China, with a history of over 30 years. The experimental results show that the BERT-assist system is a reliable second rater for massive translations in terms of translation quality, as it can effectively sift out high-quality translations with a reliability of r = 0.9 or higher. Thus, the automated translation scoring system based on BERT can satisfactorily predict the ranking of translations according to translation quality and sift out high-quality translations potentially shortlisted for prizes. Full article
(This article belongs to the Special Issue New Developments in Computational Linguistics to Support Decision Making)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop