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Novel Approaches for Natural Language Processing
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Novel Approaches for Natural Language Processing

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 January 2025 | Viewed by 3600

Special Issue Editor

Prof. Dr. Yi Guan

E-Mail Website
Guest Editor
Faculty of Computing, Harbin institute of Technology, Harbin 150001, China
Interests: medical informatics; knowledge engineering; natural language processing

Special Issue Information

Dear Colleagues,

We are inviting submissions to the upcoming Special Issue of Applied Sciences on Novel Approaches for Natural Language Processing.

Natural language is innately characterized by contextual relevance, flexibility, grammatical and semantic diversity, and interdisciplinarity, which brings great challenges to automatic processing. In recent years, methods based on deep neural networks, represented by deep learning, have achieved success in various fields of natural language processing and attracted great attention from the academic and industrial community. However, the weak theoretical foundation has also brought some negative effects, i.e., people only focus on improving system performance and no longer emphasize the theoretical basis of the method. This abuse of empiricism has adversely affected the healthy development of this discipline.

We have launched this Special Issue to promote natural language processing technology and return to the track of rationalism, draw innovation from the latest theoretical achievements of cognitive science such as psychology, linguistics, neuroscience, brain science and other emerging sciences, and propose novel approaches with consistency, concision, compatibility, simplicity, and universality. Overall, we hope to encourage the scientific community to pay greater attention to the theoretical basis and interpretability of methods while enjoying the performance dividends brought by deep learning and promote the healthy development of natural language processing technology.

In this Special Issue, we invite submissions that explore cutting-edge research and recent advances in the fields of natural language processing. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.

Prof. Dr. Yi Guan
Guest Editor

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
  • deep learning
  • rationalism
  • cognitive science

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Published Papers (2 papers)

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Research

40 pages, 652 KiB  
Article
Implementing Cognitive Semantics of Autoepistemic Membership Statements: The Case of Categories with Prototypes
by Radosław Piotr Katarzyniak, Grzegorz Popek and Marcin Żurawski
Appl. Sci. 2024, 14(4), 1609; https://doi.org/10.3390/app14041609 - 17 Feb 2024
Viewed by 859
Abstract
This article presents a model of an architecture of an artificial cognitive agent that performs the function of generating autoepistemic membership statements used to communicate beliefs about the belonging of an observed external object to a category with a prototype. The meaning of [...] Read more.
This article presents a model of an architecture of an artificial cognitive agent that performs the function of generating autoepistemic membership statements used to communicate beliefs about the belonging of an observed external object to a category with a prototype. The meaning of statements is described within the model by means of cognitive semantics. The presented proposal builds upon a pre-existing architecture and a semantic model designed for a simpler case of categories without a prototype. The main conclusion is that it is possible to develop an interactive cognitive agent capable of learning about categories with prototypes and producing autoepistemic membership statements fulfilling requirements of Rosch’s standard version of prototype semantics and satisfying pragmatic and logical rules for generating equivalents of these statements in natural languages. Detailed results include the following: an original proposal for an agent’s architecture, a model of an agent’s strategy of learning categories with a prototype, a scheme for determining the computational complexity of particular implementations of the learning strategy, definitions of cognitive semantics for particular cases of autoepistemic membership statements, and an analytical verification of properties of the proposed cognitive semantics. Finally, this article discusses the directions of further development and potential variants of the proposed architecture. Full article
(This article belongs to the Special Issue Novel Approaches for Natural Language Processing)
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20 pages, 634 KiB  
Article
Detection of the Severity Level of Depression Signs in Text Combining a Feature-Based Framework with Distributional Representations
by Sergio Muñoz and Carlos Á. Iglesias
Appl. Sci. 2023, 13(21), 11695; https://doi.org/10.3390/app132111695 - 26 Oct 2023
Cited by 4 | Viewed by 2161
Abstract
Depression is a common and debilitating mental illness affecting millions of individuals, diminishing their quality of life and overall well-being. The increasing prevalence of mental health disorders has underscored the need for innovative approaches to detect and address depression. In this context, text [...] Read more.
Depression is a common and debilitating mental illness affecting millions of individuals, diminishing their quality of life and overall well-being. The increasing prevalence of mental health disorders has underscored the need for innovative approaches to detect and address depression. In this context, text analysis has emerged as a promising avenue. Novel solutions for text-based depression detection commonly rely on deep neural networks or transformer-based models. Although these approaches have yielded impressive results, they often come with inherent limitations, such as substantial computational requirements or a lack of interpretability. This work aims to bridge the gap between substantial performance and practicality in the detection of depression signs within digital content. To this end, we introduce a comprehensive feature framework that integrates linguistic signals, emotional expressions, and cognitive patterns. The combination of this framework with distributional representations contributes to fostering the understanding of language patterns indicative of depression and provides a deeper grasp of contextual nuances. We exploit this combination using traditional machine learning methods in an effort to yield substantial performance without compromising interpretability and computational efficiency. The performance and generalizability of our approach have been assessed through experimentation using multiple publicly available English datasets. The results demonstrate that our method yields throughput on par with more complex and resource-intensive solutions, achieving F1-scores above 70%. This accomplishment is notable, as the proposed method simultaneously preserves the virtues of simplicity, interpretability, and reduced computational overhead. In summary, the findings of this research contribute to the field by offering an accessible and scalable solution for the detection of depression in real-world scenarios. Full article
(This article belongs to the Special Issue Novel Approaches for Natural Language Processing)
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