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

Deadline for manuscript submissions: closed (10 February 2025) | Viewed by 12526

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Special Issue Editors

Dr. Carlos A. Iglesias

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Guest Editor

Intelligent Systems Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: multiagent systems; social computing; linked data; natural language processing; affect technology; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Jinyi Zhang

E-Mail Website
Guest Editor Assistant

1. Faculty of Engineering, Gifu University, Gifu, Japan
2. School of Information Science and Engineering, Shenyang Ligong University, Shenyang, China
Interests: deep learning; natural language processing; large language models; neural machine translation; corpus construction; data augmentation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The vibrant era of digital information has experienced a paradigm shift with the integration of machine learning in the domain of Natural Language Processing (NLP). The surge in data-driven decision making and human-like interface applications underscores the prominence and ubiquity of these technologies. This Special Issue seeks to delve deep into the recent applications of machine learning within the realm of NLP, elucidating its transformative potential in reshaping the human–computer interaction paradigm.

NLP, at its core, encompasses a spectrum of tasks ranging from sentiment analysis, which discerns underlying emotions from textual data, to voice analysis and processing, enhancing auditory interactions. The granular identification capabilities of entity recognition play a pivotal role in information retrieval, while syntax analysis helps in understanding the structural intricacies of language. Furthermore, we are witnessing revolutionary changes in the domain of machine translation and summarization, making cross-cultural and multilingual interactions seamless. Large Language Models, with their massive information processing capabilities, are augmenting question-answering systems, chatbots, and conversational agents, facilitating more organic and intuitive interactions. Additionally, the confluence of image semantic segmentation with NLP has opened avenues for more comprehensive multimedia understanding.

Relevant areas of exploration within this amalgamation of machine learning and NLP include, but are not restricted to:

Sentiment Analysis;
Voice Analysis and Processing;
Entity Recognition;
Syntax Analysis;
Machine Translation and Summarization;
Large Language Models;
Question Answering;
Chatbots and Conversational Agents;
Image Semantic Segmentation.

In the spirit of knowledge dissemination and fostering innovation, this Special Issue aims to amass high-quality, original research papers that elucidate the recent applications, challenges, and future prospects of machine learning in NLP.

Dr. Carlos A. Iglesias
Guest Editor

Dr. Jinyi Zhang
Guest Editor Assistant

Manuscript Submission Information

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Keywords

sentiment analysis
voice analysis and processing
entity recognition
syntax analysis
machine translation and summarization
large language models
question answering
chatbots and conversational agents
image semantic segmentation

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Published Papers (8 papers)

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Research

19 pages, 573 KiB

Open AccessArticle

Advancing Few-Shot Named Entity Recognition with Large Language Model

by Yuhui Xiao, Jianjian Zou and Qun Yang

Appl. Sci. 2025, 15(7), 3838; https://doi.org/10.3390/app15073838 - 1 Apr 2025

Viewed by 467

Abstract

Few-shot named entity recognition (NER) involves identifying specific entities using limited data. Metric learning-based methods, which compute token-level similarities between query and support sets to identify target entities, have demonstrated remarkable performance in this task. However, their effectiveness deteriorates when the distribution of the support set differs from that of the query set. To address this issue, we propose a novel approach that leverages the synergy between the large language model (LLM) and the metric learning-based few-shot NER approach. Specifically, we use the LLM to refine low-confidence predictions produced by the metric learning-based few-shot NER model, thus improving overall recognition accuracy. To further reduce the difficulty of entity classification, we introduce multiple label-filtering strategies to reduce the difficulty for LLMs in performing entity classification. Furthermore, we explore the impact of prompt design on enhancing NER performance. Experimental results show that the proposed method increases the micro-F1 score on Few-NERD and CrossNER by 0.86% and 4.9%, respectively, compared to previous state-of-the-art methods. Full article

(This article belongs to the Special Issue Recent Applications of Machine Learning in Natural Language Processing (NLP))

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32 pages, 6581 KiB

Open AccessArticle

Unveiling Technological Evolution with a Patent-Based Dynamic Topic Modeling Framework: A Case Study of Advanced 6G Technologies

by Jieru Jiang, Fangli Ying and Riyad Dhuny

Appl. Sci. 2025, 15(7), 3783; https://doi.org/10.3390/app15073783 - 30 Mar 2025

Viewed by 453

Abstract

As the next frontier in wireless communication, the landscape of 6G technologies is characterized by its rapid evolution and increasing complexity, driven by the need to address global challenges such as ubiquitous connectivity, ultra-high data rates, and intelligent applications. Given the significance of 6G in shaping the future of communication and its potential to revolutionize various industries, understanding the technological evolution within this domain is crucial. Traditional topic modeling approaches fall short in adapting to the rapidly changing and highly complex nature of patent-based topic analysis in this field, thereby impeding a comprehensive understanding of the advanced technological evolution in terms of capturing temporal changes and uncovering semantic relationships. This study delves into the exploration of the evolving technologies of 6G in patent data through a novel dynamic topic modeling framework. Specifically, this work harnesses the power of large language models to effectively reduce the noise in patent data pre-processing using a prompt-based summarization technique. Then, we propose an enhanced dynamic topic modeling framework based on BERTopic to capture the time-aware features of evolving topics across periods. Additionally, we conduct comparative analysis in contextual embedding techniques and leverage SBERT pre-trained on patent data to extract the content semantics in domain-specific patent data within this framework. Finally, we apply the weak signal analysis method to identify the emerging topics in 6G technology over the periods, which makes the topic evolution analysis more interpretable than traditional topic modeling methods. The empirical results, which were validated by human experts, show that the proposed method can effectively uncover patterns of technological evolution, thus enabling its potential application to enhance strategic decision-making and stay ahead in the highly competitive and rapidly evolving technological sector. Full article

(This article belongs to the Special Issue Recent Applications of Machine Learning in Natural Language Processing (NLP))

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27 pages, 19799 KiB

Open AccessArticle

Video Temporal Grounding with Multi-Model Collaborative Learning

by Yun Tian, Xiaobo Guo, Jinsong Wang, Bin Li and Shoujun Zhou

Appl. Sci. 2025, 15(6), 3072; https://doi.org/10.3390/app15063072 - 12 Mar 2025

Viewed by 587

Abstract

Given an untrimmed video and a natural language query, the video temporal grounding task aims to accurately locate the target segment within the video. Functioning as a critical conduit between computer vision and natural language processing, this task holds profound importance in advancing video comprehension. Current research predominantly centers on enhancing the performance of individual models, thereby overlooking the extensive possibilities afforded by multi-model synergy. While knowledge flow methods have been adopted for multi-model and cross-modal collaborative learning, several critical concerns persist, including the unidirectional transfer of knowledge, low-quality pseudo-label generation, and gradient conflicts inherent in cooperative training. To address these issues, this research proposes a Multi-Model Collaborative Learning (MMCL) framework. By incorporating a bidirectional knowledge transfer paradigm, the MMCL framework empowers models to engage in collaborative learning through the interchange of pseudo-labels. Concurrently, the mechanism for generating pseudo-labels is optimized using the CLIP model’s prior knowledge, bolstering both the accuracy and coherence of these labels while efficiently discarding extraneous temporal fragments. The framework also integrates an iterative training algorithm for multi-model collaboration, mitigating gradient conflicts through alternate optimization and achieving a dynamic balance between collaborative and independent learning. Empirical evaluations across multiple benchmark datasets indicate that the MMCL framework markedly elevates the performance of video temporal grounding models, exceeding existing state-of-the-art approaches in terms of mIoU and Rank@1. Concurrently, the framework accommodates both homogeneous and heterogeneous model configurations, demonstrating its broad versatility and adaptability. This investigation furnishes an effective avenue for multi-model collaborative learning in video temporal grounding, bolstering efficient knowledge dissemination and charting novel pathways in the domain of video comprehension. Full article

(This article belongs to the Special Issue Recent Applications of Machine Learning in Natural Language Processing (NLP))

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34 pages, 2193 KiB

Open AccessArticle

Fine-Tuning Large Language Models for Ontology Engineering: A Comparative Analysis of GPT-4 and Mistral

by Dimitrios Doumanas, Andreas Soularidis, Dimitris Spiliotopoulos, Costas Vassilakis and Konstantinos Kotis

Appl. Sci. 2025, 15(4), 2146; https://doi.org/10.3390/app15042146 - 18 Feb 2025

Viewed by 1777

Abstract

Ontology engineering (OE) plays a critical role in modeling and managing structured knowledge across various domains. This study examines the performance of fine-tuned large language models (LLMs), specifically GPT-4 and Mistral 7B, in efficiently automating OE tasks. Foundational OE textbooks are used as the basis for dataset creation and for feeding the LLMs. The methodology involved segmenting texts into manageable chapters, generating question–answer pairs, and translating visual elements into description logic to curate fine-tuned datasets in JSONL format. This research aims to enhance the models’ abilities to generate domain-specific ontologies, with hypotheses asserting that fine-tuned LLMs would outperform base models, and that domain-specific datasets would significantly improve their performance. Comparative experiments revealed that GPT-4 demonstrated superior accuracy and adherence to ontology syntax, albeit with higher computational costs. Conversely, Mistral 7B excelled in speed and cost efficiency but struggled with domain-specific tasks, often generating outputs that lacked syntactical precision and relevance. The presented results highlight the necessity of integrating domain-specific datasets to improve contextual understanding and practical utility in specialized applications, such as Search and Rescue (SAR) missions in wildfire incidents. Both models, despite their limitations, exhibited potential in understanding OE principles. However, their performance underscored the importance of aligning training data with domain-specific knowledge to emulate human expertise effectively. This study, based on and extending our previous work on the topic, concludes that fine-tuned LLMs with targeted datasets enhance their utility in OE, offering insights into improving future models for domain-specific applications. The findings advocate further exploration of hybrid solutions to balance accuracy and efficiency. Full article

(This article belongs to the Special Issue Recent Applications of Machine Learning in Natural Language Processing (NLP))

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16 pages, 2188 KiB

Open AccessArticle

MCP: A Named Entity Recognition Method for Shearer Maintenance Based on Multi-Level Clue-Guided Prompt Learning

by Xiangang Cao, Luyang Shi, Xulong Wang, Yong Duan, Xin Yang and Xinyuan Zhang

Appl. Sci. 2025, 15(4), 2106; https://doi.org/10.3390/app15042106 - 17 Feb 2025

Viewed by 468

Abstract

The coal mining industry has accumulated a vast amount of knowledge on shearer accident analysis and handling during its development. Accurately identifying and extracting entity information related to shearer maintenance is crucial for advancing downstream tasks in intelligent shearer operations and maintenance. Currently, named entity recognition in the field of shearer maintenance primarily relies on fine-tuning-based methods; however, a gap exists between pretraining and downstream tasks. In this paper, we introduce prompt learning and large language models (LLMs), proposing a named entity recognition method for shearer maintenance based on multi-level clue-guided prompt learning (MCP). This method consists of three key components: (1) the prompt learning layer, which encapsulates the information to be identified and forms multi-level sub-clues into structured prompts based on a predefined format; (2) the LLM layer, which employs a decoder-only architecture-based large language model to deeply process the connection between the structured prompts and the information to be identified through multiple stacked decoder layers; and (3) the answer layer, which maps the output of the LLM layer to a structured label space via a parser to obtain the recognition results of structured named entities in the shearer maintenance domain. By designing multi-level sub-clues, MCP enables the model to extract and learn trigger words related to entity recognition from the prompts, acquiring context-aware prompt tokens. This allows the model to make accurate predictions, bridging the gap between fine-tuning and pretraining while eliminating the reliance on labeled data for fine-tuning. Validation was conducted on a self-constructed knowledge corpus in the shearer maintenance domain. Experimental results demonstrate that the proposed method outperforms mainstream baseline models in the field of shearer maintenance. Full article

(This article belongs to the Special Issue Recent Applications of Machine Learning in Natural Language Processing (NLP))

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13 pages, 349 KiB

Open AccessArticle

Multi-Task Supervised Alignment Pre-Training for Few-Shot Multimodal Sentiment Analysis

by Junyang Yang, Jiuxin Cao and Chengge Duan

Appl. Sci. 2025, 15(4), 2095; https://doi.org/10.3390/app15042095 - 17 Feb 2025

Viewed by 493

Abstract

Few-shot multimodal sentiment analysis (FMSA) has garnered substantial attention due to the proliferation of multimedia applications, especially given the frequent difficulty in obtaining large quantities of training samples. Previous works have directly incorporated vision modality into the pre-trained language model (PLM) and then leveraged prompt learning, showing effectiveness in few-shot scenarios. However, these methods encounter challenges in aligning the high-level semantics of different modalities due to their inherent heterogeneity, which impacts the performance of sentiment analysis. In this paper, we propose a novel framework called Multi-task Supervised Alignment Pre-training (MSAP) to enhance modality alignment and consequently improve the performance of multimodal sentiment analysis. Our approach uses a multi-task training method—incorporating image classification, image style recognition, and image captioning—to extract modal-shared information and stronger semantics to improve visual representation. We employ task-specific prompts to unify these diverse objectives into a single Masked Language Model (MLM), which serves as the foundation for our Multi-task Supervised Alignment Pre-training (MSAP) framework to enhance the alignment of visual and textual modalities. Extensive experiments on three datasets demonstrate that our method achieves a new state-of-the-art for the FMSA task. Full article

(This article belongs to the Special Issue Recent Applications of Machine Learning in Natural Language Processing (NLP))

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19 pages, 768 KiB

Open AccessArticle

Evaluating Retrieval-Augmented Generation Models for Financial Report Question and Answering

by Ivan Iaroshev, Ramalingam Pillai, Leandro Vaglietti and Thomas Hanne

Appl. Sci. 2024, 14(20), 9318; https://doi.org/10.3390/app14209318 - 12 Oct 2024

Cited by 3 | Viewed by 4880

Abstract

This study explores the application of retrieval-augmented generation (RAG) to improve the accuracy and reliability of large language models (LLMs) in the context of financial report analysis. The focus is on enabling private investors to make informed decisions by enhancing the question-and-answering capabilities regarding the half-yearly or quarterly financial reports of banks. The study adopts a Design Science Research (DSR) methodology to develop and evaluate an RAG system tailored for this use case. The study conducts a series of experiments to explore models in which different RAG components are used. The aim is to enhance context relevance, answer faithfulness, and answer relevance. The results indicate that model one (OpenAI ADA and OpenAI GPT-4) achieved the highest performance, showing robust accuracy and relevance in response. Model three (MiniLM Embedder and OpenAI GPT-4) scored significantly lower, indicating the importance of high-quality components. The evaluation also revealed that well-structured reports result in better RAG performance than less coherent reports. Qualitative questions received higher scores than the quantitative ones, demonstrating the RAG’s proficiency in handling descriptive data. In conclusion, a tailored RAG can aid investors in providing accurate and contextually relevant information from financial reports, thereby enhancing decision making. Full article

(This article belongs to the Special Issue Recent Applications of Machine Learning in Natural Language Processing (NLP))

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13 pages, 622 KiB

Open AccessArticle

A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis

by Ruiding Gao, Lei Jiang, Ziwei Zou, Yuan Li and Yurong Hu

Appl. Sci. 2024, 14(7), 2738; https://doi.org/10.3390/app14072738 - 25 Mar 2024

Cited by 4 | Viewed by 1361

Abstract

Aspect-level sentiment analysis is a research focal point for natural language comprehension. An attention mechanism is a very important approach for aspect-level sentiment analysis, but it only fuses sentences from a semantic perspective and ignores grammatical information in the sentences. Graph convolutional networks (GCNs) are a better method for processing syntactic information; however, they still face problems in effectively combining semantic and syntactic information. This paper presents a sentiment-supported graph convolutional network (SSGCN). This SSGCN first obtains the semantic information of the text through aspect-aware attention and self-attention; then, a grammar mask matrix and a GCN are applied to preliminarily combine semantic information with grammatical information. Afterward, the processing of these information features is divided into three steps. To begin with, features related to the semantics and grammatical features of aspect words are extracted. The second step obtains the enhanced features of the semantic and grammatical information through sentiment support words. Finally, it concatenates the two features, thus enhancing the effectiveness of the attention mechanism formed from the combination of semantic and grammatical information. The experimental results show that compared with benchmark models, the SSGCN had an improved accuracy of 6.33–0.5%. In macro F1 evaluation, its improvement range was 11.68–0.5%. Full article

(This article belongs to the Special Issue Recent Applications of Machine Learning in Natural Language Processing (NLP))

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