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Natural Language Processing and Information Retrieval, 2nd Edition

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

Deadline for manuscript submissions: 15 August 2024 | Viewed by 2552

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Special Issue Editors

Dr. Shangsong Liang

E-Mail Website
Guest Editor

School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 511400, China
Interests: information retrieval; data mining; machine learning; natural language processing
Special Issues, Collections and Topics in MDPI journals

Dr. Zaiqiao Meng

E-Mail Website
Guest Editor

School of Computing Science, University of Glasgow, Scotland G128QQ, UK
Interests: NLP; knowledge graphs; graph neural networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent decades, techniques that integrate natural language processing (NLP) and information retrieval (IR) have achieved significant improvements across a wide spectrum of real-life applications, such as question-answering summarization, neural text retrieval and understanding, and representation learning for information extraction. One of the keys to the success of these real-life applications is how NLP and IR integrate in seamless and appropriate ways.

This Special Issue is intended to provide an overview of state-of-the-art research in the fields of NLP and IR, and how they integrate and improve each other in terms of either theories or applications. Specifically, this Special Issue aims to gather works from researchers with broad expertise in NLP and IR to discuss their cutting-edge theories, models, methods, algorithms, applications, or perspectives on future directions.

The topics of interest for this Special Issue include but are not limited to:

* Question answering;

* Information retrieval and text mining;

* NLP and IR theories and applications;

* Summarization;

* Graph neural networks for NLP and IR;

* Machine/deep learning for NLP and IR;

* Machine translation and multilingualism;

* Syntax: tagging, chunking, and parsing;

* Semantics: lexical, sentence-level semantics, textual inference, and other areas;

* Generation;

* Dialogue and interactive systems;

* Search and ranking;

* NLP for search, recommendation, and representation.

Dr. Shangsong Liang
Dr. Zaiqiao Meng
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

natural language processing
information retrieval
machine learning
deep learning

Related Special Issue

Natural Language Processing and Information Retrieval in Electronics (20 articles)

Published Papers (3 papers)

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Research

15 pages, 489 KiB

Open AccessArticle

WCC-EC 2.0: Enhancing Neural Machine Translation with a 1.6M+ Web-Crawled English-Chinese Parallel Corpus

by Jinyi Zhang, Ke Su, Ye Tian and Tadahiro Matsumoto

Electronics 2024, 13(7), 1381; https://doi.org/10.3390/electronics13071381 - 5 Apr 2024

Viewed by 513

Abstract

This research introduces WCC-EC 2.0 (Web-Crawled Corpus—English and Chinese), a comprehensive parallel corpus designed for enhancing Neural Machine Translation (NMT), featuring over 1.6 million English-Chinese sentence pairs meticulously gathered via web crawling. This corpus, extracted through an advanced web crawler, showcases the vast linguistic diversity and richness of English and Chinese, uniquely spanning the rarely covered news and music domains. Our methodical approach in web crawling and corpus assembly, coupled with rigorous experiments and manual evaluations, demonstrated its superiority by achieving high BLEU scores, marking significant strides in translation accuracy and model resilience. Its inclusion of these specific areas adds significant value, providing a unique dataset that enriches the scope for NMT research and development. With the rise of NMT technology, WCC-EC 2.0 emerges not only as an invaluable resource for researchers and developers, but also as a pivotal tool for improving translation accuracy, training more resilient models, and promoting interlingual communication. Full article

(This article belongs to the Special Issue Natural Language Processing and Information Retrieval, 2nd Edition)

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12 pages, 1219 KiB

Open AccessArticle

Hierarchical Perceptual Graph Attention Network for Knowledge Graph Completion

by Wenhao Han, Xuemei Liu, Jianhao Zhang and Hairui Li

Electronics 2024, 13(4), 721; https://doi.org/10.3390/electronics13040721 - 9 Feb 2024

Viewed by 712

Abstract

Knowledge graph completion (KGC), the process of predicting missing knowledge through known triples, is a primary focus of research in the field of knowledge graphs. As an important graph representation technique in deep learning, graph neural networks (GNNs) perform well in knowledge graph completion, but most existing graph neural network-based knowledge graph completion methods tend to aggregate neighborhood information directly and individually, ignoring the rich hierarchical semantic structure of KGs. As a result, how to effectively deal with multi-level complex relations is still not well resolved. In this study, we present a hierarchical knowledge graph completion technique that combines both relation-level and entity-level attention and incorporates a weight matrix to enhance the significance of the embedded information under different semantic conditions. Furthermore, it updates neighborhood information to the central entity using a hierarchical aggregation approach. The proposed model enhances the capacity to capture hierarchical semantic feature information and is adaptable to various scoring functions as decoders, thus yielding robust results. We conducted experiments on a public benchmark dataset and compared it with several state-of-the-art models, and the experimental results indicate that our proposed model outperforms existing models in several aspects, proving its superior performance and validating the effectiveness of the model. Full article

(This article belongs to the Special Issue Natural Language Processing and Information Retrieval, 2nd Edition)

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12 pages, 420 KiB

Open AccessArticle

Improving Medical Entity Recognition in Spanish by Means of Biomedical Language Models

by Aitana Villaplana, Raquel Martínez and Soto Montalvo

Electronics 2023, 12(23), 4872; https://doi.org/10.3390/electronics12234872 - 2 Dec 2023

Viewed by 975

Abstract

Named Entity Recognition (NER) is an important task used to extract relevant information from biomedical texts. Recently, pre-trained language models have made great progress in this task, particularly in English language. However, the performance of pre-trained models in the Spanish biomedical domain has not been evaluated in an experimentation framework designed specifically for the task. We present an approach for named entity recognition in Spanish medical texts that makes use of pre-trained models from the Spanish biomedical domain. We also use data augmentation techniques to improve the identification of less frequent entities in the dataset. The domain-specific models have improved the recognition of name entities in the domain, beating all the systems that were evaluated in the eHealth-KD challenge 2021. Language models from the biomedical domain seem to be more effective in characterizing the specific terminology involved in this task of named entity recognition, where most entities correspond to the "concept" type involving a great number of medical concepts. Regarding data augmentation, only back translation has slightly improved the results. Clearly, the most frequent types of entities in the dataset are better identified. Although the domain-specific language models have outperformed most of the other models, the multilingual generalist model mBERT obtained competitive results. Full article

(This article belongs to the Special Issue Natural Language Processing and Information Retrieval, 2nd Edition)

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