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Electronics
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Zero-Shot Learning in Natural Language Processing and It’s Applications
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Zero-Shot Learning in Natural Language Processing and It’s Applications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 6392

Special Issue Editors

Dr. Long Yang

E-Mail Website
Guest Editor
Department of Computer Science, Durham University, Durham DH1 3LE, UK
Interests: machine learning; semantic analysis; natural language processing; deep learning; zero-shot learning
Dr. Noura Al Moubayed

E-Mail Website
Guest Editor
Department of Computer Science, Durham University, Durham DH1 3LE, UK
Interests: explainable machine learning; natural language processing; optimisation

Special Issue Information

Dear Colleagues,

The growing industrial demand of natural language processing (NLP) and computer vision (CV) has motivated the development of semantic–visual modelling. Zero-shot learning (ZSL) has received increasing attention in the past two decades due to its superiority in coping with novel classes and out-of-distribution (OOD) scenarios. This Special Issue focuses on how to design new NLP paradigms to enhance the generalization to 1) novel distribution tasks; 2) novel modalities, e.g., visual images; 3) novel representations, e.g., knowledge graphs. Backbone models and representation learning for general NLP tasks are out of the scope of the issue. The purpose of the issue is to thoroughly explore all possible solutions to update existing ZSL paradigms in NLP, CV, and other modalities that involve unlabelled novel classes and tasks. Publications in this Special Issue will contribute to the existing literature on benchmark establishment, paradigm design, model development, and application deployment of NLP in CV and other real-world modalities and issues.

Dr. Long Yang
Dr. Noura Al Moubayed
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

  • zero-shot learning
  • natural language processing
  • computer vision
  • machine learning
  • deep learning
  • artificial intelligence

Published Papers (4 papers)

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Research

20 pages, 801 KiB  
Article
Towards Cognition-Aligned Visual Language Models via Zero-Shot Instance Retrieval
by Teng Ma, Daniel Organisciak, Wenbao Ma and Yang Long
Electronics 2024, 13(9), 1660; https://doi.org/10.3390/electronics13091660 (registering DOI) - 25 Apr 2024
Abstract
The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can seamlessly integrate into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this [...] Read more.
The pursuit of Artificial Intelligence (AI) that emulates human cognitive processes is a cornerstone of ethical AI development, ensuring that emerging technologies can seamlessly integrate into societal frameworks requiring nuanced understanding and decision-making. Zero-Shot Instance Retrieval (ZSIR) stands at the forefront of this endeavour, potentially providing a robust platform for AI systems, particularly large visual language models, to demonstrate and refine cognition-aligned learning without the need for direct experience. In this paper, we critically evaluate current cognition alignment methodologies within traditional zero-shot learning paradigms using visual attributes and word embedding generated by large AI models. We propose a unified similarity function that quantifies the cognitive alignment level, bridging the gap between AI processes and human-like understanding. Through extensive experimentation, our findings illustrate that this similarity function can effectively mirror the visual–semantic gap, steering the model towards enhanced performance in Zero-Shot Instance Retrieval. Our models achieve state-of-the-art performance on both the SUN (92.8% and 82.2%) and CUB datasets (59.92% and 48.82%) for bi-directional image-attribute retrieval accuracy. This work not only benchmarks the cognition alignment of AI but also sets a new precedent for the development of visual language models attuned to the complexities of human cognition. Full article
(This article belongs to the Special Issue Zero-Shot Learning in Natural Language Processing and It’s Applications)
18 pages, 3070 KiB  
Article
Harnessing Causal Structure Alignment for Enhanced Cross-Domain Named Entity Recognition
by Xiaoming Liu, Mengyuan Cao, Guan Yang, Jie Liu, Yang Liu and Hang Wang
Electronics 2024, 13(1), 67; https://doi.org/10.3390/electronics13010067 - 22 Dec 2023
Viewed by 598
Abstract
Cross-domain named entity recognition (NER) is a crucial task in various practical applications, particularly when faced with the challenge of limited data availability in target domains. Existing methodologies primarily depend on feature representation or model parameter sharing mechanisms to enable the transfer of [...] Read more.
Cross-domain named entity recognition (NER) is a crucial task in various practical applications, particularly when faced with the challenge of limited data availability in target domains. Existing methodologies primarily depend on feature representation or model parameter sharing mechanisms to enable the transfer of entity recognition capabilities across domains. However, these approaches often ignore the latent causal relationships inherent in invariant features. To address this limitation, we propose a novel framework, the Causal Structure Alignment-based Cross-Domain Named Entity Recognition (CSA-NER) framework, designed to harness the causally invariant features within causal structures to enhance the cross-domain transfer of entity recognition competence. Initially, CSA-NER constructs a causal feature graph utilizing causal discovery to ascertain causal relationships between entities and contextual features across source and target domains. Subsequently, it performs graph structure alignment to extract causal invariant knowledge across domains via the graph optimal transport (GOT) method. Finally, the acquired causal invariant knowledge is refined and utilized through the integration of Gated Attention Units (GAUs). Comprehensive experiments conducted on five English datasets and a specific CD-NER dataset exhibit a notable improvement in the average performance of the CSA-NER model in comparison to existing cross-domain methods. These findings underscore the significance of unearthing and employing latent causal invariant knowledge to effectively augment the entity recognition capabilities in target domains, thereby contributing a robust methodology to the broader realm of cross-domain natural language processing. Full article
(This article belongs to the Special Issue Zero-Shot Learning in Natural Language Processing and It’s Applications)
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14 pages, 6184 KiB  
Article
Explainable B2B Recommender System for Potential Customer Prediction Using KGAT
by Gyungah Cho, Pyoung-seop Shim and Jaekwang Kim
Electronics 2023, 12(17), 3536; https://doi.org/10.3390/electronics12173536 - 22 Aug 2023
Viewed by 1211
Abstract
The adoption of recommender systems in business-to-business (B2B) can make the management of companies more efficient. Although the importance of recommendation is increasing with the expansion of B2B e-commerce, not enough studies on B2B recommendations have been conducted. Due to several differences between [...] Read more.
The adoption of recommender systems in business-to-business (B2B) can make the management of companies more efficient. Although the importance of recommendation is increasing with the expansion of B2B e-commerce, not enough studies on B2B recommendations have been conducted. Due to several differences between B2B and business-to-consumer (B2C), the B2B recommender system should be defined differently. This paper presents a new perspective on the explainable B2B recommender system using the knowledge graph attention network for recommendation (KGAT). Unlike traditional recommendation systems that suggest products to consumers, this study focuses on recommending potential buyers to sellers. Additionally, the utilization of the KGAT attention mechanisms enables the provision of explanations for each company’s recommendations. The Korea Electronic Taxation System Association provides the Market Transaction Dataset in South Korea, and this research shows how the dataset is utilized in the knowledge graph (KG). The main tasks can be summarized in three points: (i) suggesting the application of an explainable recommender system in B2B for recommending potential customers, (ii) extracting the performance-enhancing features of a knowledge graph, and (iii) enhancing keyword extraction for trading items to improve recommendation performance. We can anticipate providing good insight into the development of the industry via the utilization of the B2B recommendation of potential customer prediction. Full article
(This article belongs to the Special Issue Zero-Shot Learning in Natural Language Processing and It’s Applications)
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22 pages, 671 KiB  
Article
LLM-Informed Multi-Armed Bandit Strategies for Non-Stationary Environments
by J. de Curtò, I. de Zarzà, Gemma Roig, Juan Carlos Cano, Pietro Manzoni and Carlos T. Calafate
Electronics 2023, 12(13), 2814; https://doi.org/10.3390/electronics12132814 - 25 Jun 2023
Cited by 5 | Viewed by 3789
Abstract
In this paper, we introduce an innovative approach to handling the multi-armed bandit (MAB) problem in non-stationary environments, harnessing the predictive power of large language models (LLMs). With the realization that traditional bandit strategies, including epsilon-greedy and upper confidence bound (UCB), may struggle [...] Read more.
In this paper, we introduce an innovative approach to handling the multi-armed bandit (MAB) problem in non-stationary environments, harnessing the predictive power of large language models (LLMs). With the realization that traditional bandit strategies, including epsilon-greedy and upper confidence bound (UCB), may struggle in the face of dynamic changes, we propose a strategy informed by LLMs that offers dynamic guidance on exploration versus exploitation, contingent on the current state of the bandits. We bring forward a new non-stationary bandit model with fluctuating reward distributions and illustrate how LLMs can be employed to guide the choice of bandit amid this variability. Experimental outcomes illustrate the potential of our LLM-informed strategy, demonstrating its adaptability to the fluctuating nature of the bandit problem, while maintaining competitive performance against conventional strategies. This study provides key insights into the capabilities of LLMs in enhancing decision-making processes in dynamic and uncertain scenarios. Full article
(This article belongs to the Special Issue Zero-Shot Learning in Natural Language Processing and It’s Applications)
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