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 August 2024 | Viewed by 1021

Special Issue Editors


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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

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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

Published Papers (1 paper)

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Research

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 401
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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