Deep Learning and Natural Language Processing

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 14883

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Guest Editor
Algoritmi Research Center, Informatics Department, University of Évora, 7002–554 Évora, Portugal
Interests: artificial intelligence; natural language processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last years natural language processing tasks have been able to improve significantly their performance through the use of deep learning methodologies. Machine translation, summarization, and question-answering systems are some examples of tasks that were able to reach a high level of performance.

In this special issue we welcome new research contributions and survey papers describing advances in this relevant domain, with a special focus, but not exclusively on:

  • Deep learning architectures specialized for NLP;
  • Deep learning based approaches to NLP tasks, such as, NERC, syntactic parsers, semantic analysis, semantic role labeling, information extraction, sentiment analysis, summarization, question-answering and machine translation;
  • Hybrid (symbolic + deep learning) approaches to NLP;
  • Text annotation using deep-learning approaches;
  • Survey papers.

Dr. Paulo Quaresma
Guest Editor

Manuscript Submission Information

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Keywords

  • deep learning
  • natural language processing

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Published Papers (5 papers)

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Research

21 pages, 12914 KiB  
Article
Im2Graph: A Weakly Supervised Approach for Generating Holistic Scene Graphs from Regional Dependencies
by Swarnendu Ghosh, Teresa Gonçalves and Nibaran Das
Future Internet 2023, 15(2), 70; https://doi.org/10.3390/fi15020070 - 10 Feb 2023
Viewed by 1576
Abstract
Conceptual representations of images involving descriptions of entities and their relations are often represented using scene graphs. Such scene graphs can express relational concepts by using sets of triplets [...] Read more.
Conceptual representations of images involving descriptions of entities and their relations are often represented using scene graphs. Such scene graphs can express relational concepts by using sets of triplets subjectpredicateobject. Instead of building dedicated models for scene graph generation, our model tends to extract the latent relational information implicitly encoded in image captioning models. We explored dependency parsing to build grammatically sound parse trees from captions. We used detection algorithms for the region propositions to generate dense region-based concept graphs. These were optimally combined using the approximate sub-graph isomorphism to create holistic concept graphs for images. The major advantages of this approach are threefold. Firstly, the proposed graph generation module is completely rule-based and, hence, adheres to the principles of explainable artificial intelligence. Secondly, graph generation can be used as plug-and-play along with any region proposition and caption generation framework. Finally, our results showed that we could generate rich concept graphs without explicit graph-based supervision. Full article
(This article belongs to the Special Issue Deep Learning and Natural Language Processing)
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14 pages, 4134 KiB  
Article
Attention-Enriched Mini-BERT Fake News Analyzer Using the Arabic Language
by Husam M. Alawadh, Amerah Alabrah, Talha Meraj and Hafiz Tayyab Rauf
Future Internet 2023, 15(2), 44; https://doi.org/10.3390/fi15020044 - 22 Jan 2023
Cited by 3 | Viewed by 2191
Abstract
Internet use resulted in people becoming more reliant on social media. Social media have become the main source of fake news or rumors. They spread uncertainty in each sector of the real world, whether in politics, sports, or celebrities’ lives—all are affected by [...] Read more.
Internet use resulted in people becoming more reliant on social media. Social media have become the main source of fake news or rumors. They spread uncertainty in each sector of the real world, whether in politics, sports, or celebrities’ lives—all are affected by the uncontrolled behavior of social media platforms. Intelligent methods used to control this fake news in various languages have already been much discussed and frequently proposed by researchers. However, Arabic grammar and language are a far more complex and crucial language to learn. Therefore, work on Arabic fake-news-based datasets and related studies is much needed to control the spread of fake news on social media and other Internet media. The current study uses a recently published dataset of Arabic fake news annotated by experts. Further, Arabic-language-based embeddings are given to machine learning (ML) classifiers, and the Arabic-language-based trained minibidirectional encoder representations from transformers (BERT) is used to obtain the sentiments of Arabic grammar and feed a deep learning (DL) classifier. The holdout validation schemes are applied to both ML classifiers and mini-BERT-based deep neural classifiers. The results show a consistent improvement in the performance of mini-BERT-based classifiers, which outperformed ML classifiers, by increasing the training data. A comparison with previous Arabic fake news detection studies is shown where results of the current study show greater improvement. Full article
(This article belongs to the Special Issue Deep Learning and Natural Language Processing)
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23 pages, 6252 KiB  
Article
Using Social Media & Sentiment Analysis to Make Investment Decisions
by Ben Hasselgren, Christos Chrysoulas, Nikolaos Pitropakis and William J. Buchanan
Future Internet 2023, 15(1), 5; https://doi.org/10.3390/fi15010005 - 23 Dec 2022
Cited by 8 | Viewed by 5185
Abstract
Making investment decisions by utilizing sentiment data from social media (SM) is starting to become a more tangible concept. There has been a broad investigation into this field of study over the last decade, and many of the findings have promising results. However, [...] Read more.
Making investment decisions by utilizing sentiment data from social media (SM) is starting to become a more tangible concept. There has been a broad investigation into this field of study over the last decade, and many of the findings have promising results. However, there is still an opportunity for continued research, firstly, in finding the most effective way to obtain relevant sentiment data from SM, then building a system to measure the sentiment, and finally visualizing it to help users make investment decisions. Furthermore, much of the existing work fails to factor SM metrics into the sentiment score effectively. This paper presents a novel prototype as a contribution to the field of study. In our work, a detailed overview of the topic is given in the form of a literature and technical review. Next, a prototype is designed and developed using the findings from the previous analysis. On top of that, a novel approach to factor SM metrics into the sentiment score is presented, with the goal of measuring the collective sentiment of the data effectively. To test the proposed approach, we only used popular stocks from the S&P500 to ensure large volumes of SM sentiment was available, adding further insight into findings, which we then discuss in our evaluation. Full article
(This article belongs to the Special Issue Deep Learning and Natural Language Processing)
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20 pages, 1618 KiB  
Article
Author Identification from Literary Articles with Visual Features: A Case Study with Bangla Documents
by Ankita Dhar, Himadri Mukherjee, Shibaprasad Sen, Md Obaidullah Sk, Amitabha Biswas, Teresa Gonçalves and Kaushik Roy
Future Internet 2022, 14(10), 272; https://doi.org/10.3390/fi14100272 - 23 Sep 2022
Cited by 3 | Viewed by 1900
Abstract
Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as [...] Read more.
Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as criminal and civil cases, cybersecurity, forensics, identification of plagiarizer, and many more. An automated system in this context can thus be very beneficial for society. In this paper, we propose a convolutional neural network (CNN)-based author identification system from literary articles. This system uses visual features along with a five-layer convolutional neural network for the identification of authors. The prime motivation behind this approach was the feasibility to identify distinct writing styles through a visualization of the writing patterns. Experiments were performed on 1200 articles from 50 authors achieving a maximum accuracy of 93.58%. Furthermore, to see how the system performed on different volumes of data, the experiments were performed on partitions of the dataset. The system outperformed standard handcrafted feature-based techniques as well as established works on publicly available datasets. Full article
(This article belongs to the Special Issue Deep Learning and Natural Language Processing)
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18 pages, 538 KiB  
Article
A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language
by Yousif A. Alhaj, Abdelghani Dahou, Mohammed A. A. Al-qaness, Laith Abualigah, Aaqif Afzaal Abbasi, Nasser Ahmed Obad Almaweri, Mohamed Abd Elaziz and Robertas  Damaševičius
Future Internet 2022, 14(7), 194; https://doi.org/10.3390/fi14070194 - 27 Jun 2022
Cited by 16 | Viewed by 2917
Abstract
We propose a novel text classification model, which aims to improve the performance of Arabic text classification using machine learning techniques. One of the effective solutions in Arabic text classification is to find the suitable feature selection method with an optimal number of [...] Read more.
We propose a novel text classification model, which aims to improve the performance of Arabic text classification using machine learning techniques. One of the effective solutions in Arabic text classification is to find the suitable feature selection method with an optimal number of features alongside the classifier. Although several text classification methods have been proposed for the Arabic language using different techniques, such as feature selection methods, an ensemble of classifiers, and discriminative features, choosing the optimal method becomes an NP-hard problem considering the huge search space. Therefore, we propose a method, called Optimal Configuration Determination for Arabic text Classification (OCATC), which utilized the Particle Swarm Optimization (PSO) algorithm to find the optimal solution (configuration) from this space. The proposed OCATC method extracts and converts the features from the textual documents into a numerical vector using the Term Frequency-Inverse Document Frequency (TF–IDF) approach. Finally, the PSO selects the best architecture from a set of classifiers to feature selection methods with an optimal number of features. Extensive experiments were carried out to evaluate the performance of the OCATC method using six datasets, including five publicly available datasets and our proposed dataset. The results obtained demonstrate the superiority of OCATC over individual classifiers and other state-of-the-art methods. Full article
(This article belongs to the Special Issue Deep Learning and Natural Language Processing)
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