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Recent Applications of Machine Learning in Natural Language Processing (NLP)

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

Deadline for manuscript submissions: 10 February 2025 | Viewed by 3681

Special Issue Editors


E-Mail Website
Guest Editor Assistant
School of Information Science and Engineering, Shenyang Ligong University, Nanping Center Road, Hunnan New District, Shenyang 110159, China
Interests: deep learning; natural language processing; neural machine translation; corpus construction and data augmentation

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

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

  • 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 (2 papers)

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Research

19 pages, 768 KiB  
Article
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
Viewed by 1199
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 [...] Read more.
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
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13 pages, 622 KiB  
Article
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
Viewed by 992
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 [...] Read more.
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
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