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Natural Language Processing (NLP) with Applications and Natural Language Understanding...
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Natural Language Processing (NLP) with Applications and Natural Language Understanding (NLU), 2nd Edition

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 4288

Editor

Prof. Dr. Rodolfo Delmonte

E-Mail Website
Guest Editor
Department of Linguistics and Comparative Cultural Studies, Ca Foscari University of Venice, 30123 Venice, Italy
Interests: natural language processing; text analysis; information extraction; computational linguistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is the 2nd Edition of the Special Issue “Natural Language Processing (NLP) with Applications and Natural Language Understanding (NLU)” (https://www.mdpi.com/journal/information/special_issues/ALNGLE7U2B). NLP is both a technology and a science, a branch of both computational linguistics and artificial intelligence. The current hyper regarding LLM has reached new heights thanks to CHATGPT and its attractive applications: how will this influence NLP, and is NLP still needed, possibly in a different form? This Special Issue will bring together researchers in all areas of NLP to discuss applications and future research directions in the field of natural language understanding for DNNs.

NLP applications are all based on the same source of knowledge, i.e., words, as even visual neural networks need words for their captions. LLM vocabularies are usually made up of the first most frequent 50,000 words or types extracted from billions of tokens, but they still produce hallucinations and biases. Part of the problem at the heart of DNN use is their lack of generalization and their inherent inability to understand what they are processing.

In this Special Issue, we will feature papers on all kinds of language-oriented applications, starting with speech recognition and synthesis, machine translation, and question answering. Below is a provisional list of the most relevant topics:

  • Sentiment Analysis from Social Media;
  • Chatbots and Smart Assistants;
  • Email Filters;
  • Text Summarization;
  • Customer Support and Analytics for Market Intelligence;
  • Online Search Engine and Autocomplete;
  • Recruitment and Hiring;
  • Auto-Correct and Next-Word Prediction;
  • Spell and Grammar Checking;
  • Text Extraction and Classification;
  • Image and Facial Recognition for/from Captions;
  • NLP for Multimedia Self-Learning Language Tools.

Traditionally, NLP techniques have varied from symbolic to statistical approaches but have always addressed linguistic content that may be constituted by phonemes or phones, morphemes or sequences of subword units, and tokens of various types and lengths, including punctuation, words, multiwords, or polywords, syntactic constituents, and dependency structures. These low-level strata make up what is currently addressed directly or indirectly by the majority of applications.

Attempts are being made to address text understanding, which belongs to higher levels of linguistic knowledge: pronominal binding, coreference resolution, quantifier raising, semantic representation in terms of AMRs, or other similar theories completed by word-sense disambiguation. Not all of these tasks are suited to current transformer-based LLMs, but they could be carried out by NLP components. Finally, we assume that, to attain reasoning that includes causal inference from knowledge of the world, lower linguistic strata should not be erased but used as a trigger for further processing. Thus, we will dedicate a separate subsection of this Special Issue to these latter topics, which may be dubbed as NLP for future applications with AI for text understanding or NLU, promoting project presentation and works in progress.

In sum, in this Special Issue, we will bring together researchers by discussing innovative applications in all fields of NLP, alongside innovative results for semantic processing for NLU.

Prof. Dr. Rodolfo Delmonte
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information 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 in current applications
  • natural language understanding for future applications
  • question answering
  • sentiment analysis from social media
  • chatbots and smart assistants
  • email filters
  • text summarization
  • customer support and analytics for market intelligence
  • online search engine and autocomplete
  • auto-correct and next-word prediction
  • spell and grammar checking
  • text extraction and classification
  • image and facial recognition for/from captions
  • NLP for multimedia self-learning language tools

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Published Papers (5 papers)

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16 pages, 2298 KB  
Article
Evaluating ASR Pipeline Configurations for Kazakh: Implications for Low-Resource Turkic Languages
by Nursultan Nyssanov, Leila Rzayeva, Alisher Batkuldin and Zhaksylyk Kozhakhmet
Information 2026, 17(7), 690; https://doi.org/10.3390/info17070690 - 15 Jul 2026
Viewed by 246
Abstract
Kazakh automatic speech recognition (ASR) presents a persistent challenge for large-scale multilingual models. This paper presents a systematic evaluation of 27 ASR pipeline configurations (three ASR models × three VAD methods × three post-processing strategies) on the Kazakh Speech Dataset (KSD), examining the [...] Read more.
Kazakh automatic speech recognition (ASR) presents a persistent challenge for large-scale multilingual models. This paper presents a systematic evaluation of 27 ASR pipeline configurations (three ASR models × three VAD methods × three post-processing strategies) on the Kazakh Speech Dataset (KSD), examining the contribution of model fine-tuning, voice activity detection (VAD) preprocessing, and large language model (LLM) post-correction and benchmarking the resulting pipelines against two non-Whisper foundation models. Language-specific fine-tuning reduces Word Error Rate (WER) from 43.20% (generic Whisper-large-v3) to 11.88% (Kazakh fine-tuned Whisper-turbo), a 31.32-percentage-point absolute reduction (72.5% relative; p < 0.001, bootstrap test); the effect persists after controlling for model size (generic Whisper-large-v3-turbo, 18.92%, vs. the same architecture after fine-tuning, 11.88%; p < 0.001). VAD preprocessing consistently degrades performance. Zero-shot post-correction with general-purpose LLMs yields no benefit and adds substantial latency: Gemma-2-9B and Qwen2.5-7B raise WER by 5.5 and 7.2 percentage points at real-time factors of 0.52 and 0.30, and a larger 32B model still degrades accuracy (+10.8 points), indicating that scale is not the limiting factor. Among all systems evaluated, a larger multilingual foundation model, SeamlessM4T-v2 (9.72% WER), outperforms the fine-tuned Whisper, showing that for Kazakh model coverage matters more than pipeline engineering. Character-level error analysis identifies systematic confusion between Kazakh-specific and Russian Cyrillic characters as a dominant error source. These findings establish that, for Kazakh under the evaluated conditions, model choice dominates pipeline add-ons: fine-tuning is essential, VAD and zero-shot LLM correction consistently hurt, and a strong multilingual model sets the best result; we further discuss the extent to which these conclusions extend to typologically similar Kipchak-Turkic languages. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) with Applications and Natural Language Understanding (NLU), 2nd Edition)
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11 pages, 296 KB  
Article
Automating Systematic Reviews in Clinical Psychiatry: Comparing Domain Experts and NLP-Based Text Mining
by Cyril S. Ku, Daniel Weiner, Meera Wells, Andrew Huang and Morgan R. Peltier
Information 2026, 17(5), 463; https://doi.org/10.3390/info17050463 - 9 May 2026
Viewed by 484
Abstract
Objective: This study examines the potential of natural language processing and text mining to automate the systematic review process in clinical psychiatry, a field that traditionally relies on domain experts and can be time-consuming, prone to human bias and errors. The study compares [...] Read more.
Objective: This study examines the potential of natural language processing and text mining to automate the systematic review process in clinical psychiatry, a field that traditionally relies on domain experts and can be time-consuming, prone to human bias and errors. The study compares the classification of review articles by domain experts with that facilitated by machine algorithms. Methods: Using data from PubMed, 160 abstracts related to “transcranial magnetic stimulation” and “autism” were classified into “treatment” and “non-treatment” categories by both human reviewers and a computer algorithm. The computer algorithm, employing topic modeling in text mining, was compared to human reviewers, including two psychiatrists, a biostatistician, and a medical student. Results: The accuracy of human classifications ranged from 68% to 85%, with inter-rater reliability (Kappa statistic) between 0.40 (fair to moderate) and 0.64 (substantial). Intra-rater reliability, tested by reclassification after three months, varied from 0.38 to 0.82. Conclusions: The findings highlight the consistency and reproducibility of computational approaches compared to human classification, which exhibited both inter-rater and intra-rater variability. Differences in reviewer performance were observed; however, these patterns should be interpreted cautiously, as the study was not designed to directly assess cognitive or decision-making processes. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) with Applications and Natural Language Understanding (NLU), 2nd Edition)
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31 pages, 1573 KB  
Article
Generalised Cross-Dialectal Arabic Question Answering Through Adaptive Code-Mixed Data Augmentation
by Maha Jarallah Althobaiti
Information 2026, 17(2), 139; https://doi.org/10.3390/info17020139 - 1 Feb 2026
Viewed by 638
Abstract
Modern Standard Arabic (MSA) and the many regional dialects differ substantially in vocabulary, morphology, and pragmatic usage. Most available annotated resources are in MSA, and zero-shot transfer from MSA to dialectal tasks suffers a large performance drop. This paper addresses generalised cross-dialectal Arabic [...] Read more.
Modern Standard Arabic (MSA) and the many regional dialects differ substantially in vocabulary, morphology, and pragmatic usage. Most available annotated resources are in MSA, and zero-shot transfer from MSA to dialectal tasks suffers a large performance drop. This paper addresses generalised cross-dialectal Arabic question answering (QA), where the context and the question are written in different Arabic varieties. We propose a training-free augmentation framework that generates code-mixed questions to bridge lexical gaps across Arabic varieties. The method produces semantically faithful, balanced code-mixed questions through the following two-stage procedure: lexicon-based partial substitution with semantic similarity and substitution-rate constraints, followed by fallback neural machine translation with word-level alignment when needed. We also introduce automated multidialectal lexicon construction using machine translation, embedding-based alignment, and semantic checks. We carry out our evaluation in a zero-shot setting, where the model is fine-tuned only on MSA and then tested on dialectal inputs using ArDQA, covering five Arabic varieties and three domains (SQuAD, Vlogs, and Narratives). Experiments show consistent improvements under context-question dialect mismatch as follows: +1.09 F1/+0.87 EM on SQuAD, +1.54/+1.25 on Vlogs, and +2.75/+2.27 on Narratives, with the largest gains for Maghrebi questions in Narratives (+12.13 F1/+8.45 EM). These results show that our method improves zero-shot cross-dialectal transfer without fine-tuning or retraining. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) with Applications and Natural Language Understanding (NLU), 2nd Edition)
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16 pages, 452 KB  
Article
Validating the Use of Natural Language Processing and Text Mining for Hospital-Based Violence Intervention Programs and Criminal Justice Articles
by Cyril S. Ku, Katheryne Pugliese, Jared R. Dmello, Morgan R. Peltier, Robert Green and Sheetal Ranjan
Information 2025, 16(12), 1098; https://doi.org/10.3390/info16121098 - 11 Dec 2025
Cited by 1 | Viewed by 1181
Abstract
Hospital-based violence intervention programs (HVIPs) are a form of community violence intervention designed to address trauma resulting from violent injuries. This public health approach has been implemented across the United States since the 1990s, with numerous qualitative and quantitative studies evaluating its effectiveness. [...] Read more.
Hospital-based violence intervention programs (HVIPs) are a form of community violence intervention designed to address trauma resulting from violent injuries. This public health approach has been implemented across the United States since the 1990s, with numerous qualitative and quantitative studies evaluating its effectiveness. Manual systematic reviews by domain experts have helped identify major themes and research gaps. While these reviews are valuable for synthesizing the existing literature, thisprocess can be time-consuming and labor-intensive, given the vast amount of research in public health and criminal justice. To meet the urgent need for accessible insights into the violence-related literature, more efficient methods are essential. Recent advances in artificial intelligence (AI) offer promising tools to streamline this process. This study applies AI, specifically natural language processing techniques, to analyze recurring themes in the HVIP-related literature at the intersection of criminal justice and public health. The findings indicate that text-mining methods can enhance and accelerate the systematic review process, while also revealing new insights. The results underscore the potential of AI-driven tools to support evidence-based practices and highlight the importance of interdisciplinary collaboration to improve the effectiveness and implementation of HVIPs. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) with Applications and Natural Language Understanding (NLU), 2nd Edition)
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18 pages, 272 KB  
Article
Measuring Narrative Complexity Among Suicide Deaths in the National Violent Death Reporting System (2003–2021 NVDRS)
by Christina Chance, Alina Arseniev-Koehler, Vickie M. Mays, Kai-Wei Chang and Susan D. Cochran
Information 2025, 16(11), 989; https://doi.org/10.3390/info16110989 - 15 Nov 2025
Viewed by 1076
Abstract
A widely used repository of violent death records is the U.S. Centers for Disease Control National Violent Death Reporting System (NVDRS). The NVDRS includes narrative data, which researchers frequently utilize to go beyond its structured variables. Prior work has shown that NVDRS narratives [...] Read more.
A widely used repository of violent death records is the U.S. Centers for Disease Control National Violent Death Reporting System (NVDRS). The NVDRS includes narrative data, which researchers frequently utilize to go beyond its structured variables. Prior work has shown that NVDRS narratives vary in length depending on decedent and incident characteristics, including race/ethnicity. Whether these length differences reflect differences in narrative information potential is unclear. We use the 2003–2021 NVDRS to investigate narrative length and complexity measures among 300,323 suicides varying in decedent and incident characteristics. To do so, we operationalized narrative complexity using three manifest measures: word count, sentence count, and dependency tree depth. We then employed regression methods to predict word counts and narrative complexity scores from decedent and incident characteristics. Both were consistently lower for black non-Hispanic decedents compared to white, non-Hispanic decedents. Although narrative complexity is just one aspect of narrative information potential, these findings suggest that the information in NVDRS narratives is more limited for some racial/ethnic minorities. Future studies, possibly leveraging large language models, are needed to develop robust measures to aid in determining whether narratives in the NVDRS have achieved their stated goal of fully describing the circumstances of suicide. Full article
(This article belongs to the Special Issue Natural Language Processing (NLP) with Applications and Natural Language Understanding (NLU), 2nd Edition)
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