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Electronics
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Advances in Information Retrieval and Natural Language Processing
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Advances in Information Retrieval and Natural Language Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 5028

Special Issue Editors

Dr. Haifeng Sun

E-Mail Website
Guest Editor
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: natural language processing; knowledge acquisition; information retrieval; machine learning
Dr. Zirui Zhuang

E-Mail Website
Guest Editor
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: deep learning, reinforcement learning, graph neural network

Special Issue Information

Dear Colleagues,

In recent decades, due to the rapid development of pre-trained model technology, information retrieval (IR) and natural language processing (NLP) have achieved significant improvements in wide areas, such as conversational systems, retrieval, ranking, knowledge acquisition, and representation learning for information extraction. The idea of pre-trained models with self-supervised language modeling is playing a more and more significant role. As is demonstrated in recent works, pre-trained models are able to capture a decent amount of linguistic knowledge as well as factual knowledge, which are beneficial for downstream tasks and avoid learning such knowledge from scratch.

This Special Issue is intended to collect emerging contributions in the area of IR and NLP with the discussion on advanced technology to integrate and improve each other in terms of theories, models, and applications. The topics of interest for this Special Issue include, but are not limited to:

  • Queries and query analysis
  • Web search
  • Efficiency and scalability
  • Document representation and content analysis
  • Question answering
  • Conversational systems
  • Knowledge representation and reasoning
  • Interpretability and Analysis of Models for NLP
  • Sentence-level Semantics
  • Textual Inference
  • Evaluation Methodology
  • Fairness, accountability, transparency
  • Ethics, economics, and politics
  • Other applications and domains

Dr. Haifeng Sun
Dr. Zirui Zhuang
Guest Editors

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. Electronics 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

  • information retrieval
  • natural language processing
  • pre-trained models
  • deep learning

Published Papers (4 papers)

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Research

20 pages, 734 KiB  
Article
Integrated Model Text Classification Based on Multineural Networks
by Wenjin Hu, Jiawei Xiong, Ning Wang, Feng Liu, Yao Kong and Chaozhong Yang
Electronics 2024, 13(2), 453; https://doi.org/10.3390/electronics13020453 - 22 Jan 2024
Viewed by 824
Abstract
Based on the original deep network architecture, this paper replaces the deep integrated network by integrating shallow FastText, a bidirectional gated recurrent unit (GRU) network and the convolutional neural networks (CNNs). In FastText, word embedding, 2-grams and 3-grams are combined to extract text [...] Read more.
Based on the original deep network architecture, this paper replaces the deep integrated network by integrating shallow FastText, a bidirectional gated recurrent unit (GRU) network and the convolutional neural networks (CNNs). In FastText, word embedding, 2-grams and 3-grams are combined to extract text features. In recurrent neural networks (RNNs), a bidirectional GRU network is used to lessen information loss during the process of transmission. In CNNs, text features are extracted using various convolutional kernel sizes. Additionally, three optimization algorithms are utilized to improve the classification capabilities of each network architecture. The experimental findings using the social network news dataset demonstrate that the integrated model is effective in improving the accuracy of text classification. Full article
(This article belongs to the Special Issue Advances in Information Retrieval and Natural Language Processing)
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18 pages, 752 KiB  
Article
Automated Assessment of Initial Answers to Questions in Conversational Intelligent Tutoring Systems: Are Contextual Embedding Models Really Better?
by Colin M. Carmon, Brent Morgan, Xiangen Hu and Arthur C. Graesser
Electronics 2023, 12(17), 3654; https://doi.org/10.3390/electronics12173654 - 30 Aug 2023
Viewed by 858
Abstract
This paper assesses the ability of semantic text models to assess student responses to electronics questions compared with that of expert human judges. Recent interest in text similarity has led to a proliferation of models that can potentially be used for assessing student [...] Read more.
This paper assesses the ability of semantic text models to assess student responses to electronics questions compared with that of expert human judges. Recent interest in text similarity has led to a proliferation of models that can potentially be used for assessing student responses. However, it is unclear whether these models perform as well as early models of distributional semantics. We assessed 5166 response pairings of 219 participants across 118 electronics questions and scored each with 13 different computational text models, including models that use Regular Expressions, distributional semantics, embeddings, contextual embeddings, and combinations of these features. Regular Expressions performed the best out of the stand-alone models. We show other semantic text models performing comparably to the Latent Semantic Analysis model that was originally used for the current task, and in a small number of cases outperforming the model. Models trained on a domain-specific electronics corpus for the task performed better than models trained on general language or Newtonian physics. Furthermore, semantic text models combined with RegEx outperformed stand-alone models in agreement with human judges. Tuning the performance of these recent models in Automatic Short Answer Grading tasks for conversational intelligent tutoring systems requires empirical analysis, especially in domain-specific areas such as electronics. Therefore, the question arises as to how well recent contextual embedding models compare with earlier distributional semantic language models on this task of answering questions about electronics. These results shed light on the selection of appropriate computational techniques for text modeling to improve the accuracy, recall, weighted agreement, and ultimately the effectiveness of automatic scoring in conversational ITSs. Full article
(This article belongs to the Special Issue Advances in Information Retrieval and Natural Language Processing)
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10 pages, 1181 KiB  
Article
Text3D: 3D Convolutional Neural Networks for Text Classification
by Jinrui Wang, Jie Li and Yirui Zhang
Electronics 2023, 12(14), 3087; https://doi.org/10.3390/electronics12143087 - 16 Jul 2023
Cited by 2 | Viewed by 1310
Abstract
Convolutional Neural Networks (CNNs) have demonstrated promising performance in many NLP tasks owing to their excellent local feature-extraction capability. Many previous works have made word-level 2D CNNs deeper to capture global representations of text. Three-dimensional CNNs perform excellently in CV tasks through spatiotemporal [...] Read more.
Convolutional Neural Networks (CNNs) have demonstrated promising performance in many NLP tasks owing to their excellent local feature-extraction capability. Many previous works have made word-level 2D CNNs deeper to capture global representations of text. Three-dimensional CNNs perform excellently in CV tasks through spatiotemporal feature learning, though they are little utilized in text classification task. This paper proposes a simple, yet effective, approach for hierarchy feature learning using 3D CNN in text classification tasks, named Text3D. Text3D efficiently extracts rich information through text representations structured in three dimensions produced by pretrained language model BERT. Specifically, our Text3D utilizes word order, word embedding and hierarchy information of BERT encoder layers as features of three dimensions. The proposed model with 12 layers outperforms the baselines on four benchmark datasets for sentiment classification and topic categorization. Text3D with a different hierarchy of output from BERT layers demonstrates that the linguistic features from different layers have varied effects on text classification. Full article
(This article belongs to the Special Issue Advances in Information Retrieval and Natural Language Processing)
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15 pages, 1542 KiB  
Article
Deep Clustering by Graph Attention Contrastive Learning
by Ming Liu, Cong Liu, Xiaoyuan Fu, Jing Wang, Jiankun Li, Qi Qi and Jianxin Liao
Electronics 2023, 12(11), 2489; https://doi.org/10.3390/electronics12112489 - 31 May 2023
Cited by 1 | Viewed by 1579
Abstract
Contrastive learning shows great potential in deep clustering. It uses constructed pairs to discover the feature distribution that is required for the clustering task. In addition to conventional augmented pairs, recent methods have introduced more methods of creating highly confident pairs, such as [...] Read more.
Contrastive learning shows great potential in deep clustering. It uses constructed pairs to discover the feature distribution that is required for the clustering task. In addition to conventional augmented pairs, recent methods have introduced more methods of creating highly confident pairs, such as nearest neighbors, to provide more semantic prior knowledge. However, existing works only use partial pairwise similarities to construct semantic pairs locally without capturing the entire sample’s relationships from a global perspective. In this paper, we propose a novel clustering framework called graph attention contrastive learning (GACL) to aggregate more semantic information. To this end, GACL is designed to simultaneously perform instance-level and graph-level contrast. Specifically, with its novel graph attention mechanism, our model explores more undiscovered pairs and selectively focuses on informative pairs. To ensure local and global clustering consistency, we jointly use the designed graph-level and instance-level contrastive losses. Experiments on six challenging image benchmarks demonstrate the superiority of our proposed approach over state-of-the-art methods. Full article
(This article belongs to the Special Issue Advances in Information Retrieval and Natural Language Processing)
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