Special Issue "Creative and Generative Natural Language Processing and Its Applications"

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

Deadline for manuscript submissions: 10 November 2021.

Special Issue Editors

Dr. Krzysztof Wołk
E-Mail Website
Guest Editor
Department of Multimedia, Polish-Japanese Academy of Information Technology, 02-008 Warszawa, Poland
Interests: natural language processing; machine learning; neural networks; corpora processing; artificial intelligence; telemedicine; user experience
Dr. Ida Skubis
E-Mail Website
Guest Editor
Institute of Education and Communication Research, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: ethics in AI; chatbots; human–chatbot interaction; IE; NER; NLP; ML; MT; corpus linguistics; pluricentrism; specialized language
Dr. Tomasz Grzes
E-Mail Website
Guest Editor
Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland
Interests: embedded systems; FPGA; Artificial Intelligence; rough sets

Special Issue Information

Dear Colleagues,

This Special Issue will focus on creative natural language usage and its applications. Whereas others investigate typical areas from the field of NLP, such as parsers, taggers, etc., we will focus our attention on creative and generative research topics not limited to corpora augmentation, creative text generation, natural language understanding, answer generation, dialog agents, etc. We will also focus on its applications in other fields on science and business, especially as far as business costs reductions are concerned. We also invite convergent research articles, particularly if the generally perceived state of the art has not been established yet. We encourage you to submit papers in the areas of (but not limited to): general natural language processing, machine learning, computational and corpora based linguistics, natural language generation and understanding, fake texts detection and generation (e.g., story, poetry, QA answers, chatbot contextual answers, etc.), machine translation and its post-editing, speech technology, ASR automatic transcription and its post editing, summary generation, creative describing, and social signal processing.

Technical Program Committee Members:

1. (PhD Candidate) MSc Karol Chlasta: Polish-Japanese Academy of Information Technology, 02-008 Warsaw; Poland & SWPS University, 03-815 Warsaw, Poland
2. (PhD Candidate) MSc Eng. Emilia Zawadzka-Gosk: Polish-Japanese Academy of Information Technology, 02-008 Warsaw, Poland

Dr. Krzysztof Wołk
Dr. Ida Skubis
Dr. Tomasz Grzes
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 papers will be 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 1800 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
  • Computational linguistics
  • Corpora language processing
  • Creative text generation
  • Machine translation
  • Automatic post-editing
  • Answer generation methods and applications
  • Corpora augmentation
  • Corpora creation processing and analysis
  • Language generation applications

Published Papers (1 paper)

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Research

Article
Generation of Cross-Lingual Word Vectors for Low-Resourced Languages Using Deep Learning and Topological Metrics in a Data-Efficient Way
Electronics 2021, 10(12), 1372; https://doi.org/10.3390/electronics10121372 - 08 Jun 2021
Viewed by 445
Abstract
Linguists have been focused on a qualitative comparison of the semantics from different languages. Evaluation of the semantic interpretation among disparate language pairs like English and Tamil is an even more formidable task than for Slavic languages. The concept of word embedding in [...] Read more.
Linguists have been focused on a qualitative comparison of the semantics from different languages. Evaluation of the semantic interpretation among disparate language pairs like English and Tamil is an even more formidable task than for Slavic languages. The concept of word embedding in Natural Language Processing (NLP) has enabled a felicitous opportunity to quantify linguistic semantics. Multi-lingual tasks can be performed by projecting the word embeddings of one language onto the semantic space of the other. This research presents a suite of data-efficient deep learning approaches to deduce the transfer function from the embedding space of English to that of Tamil, deploying three popular embedding algorithms: Word2Vec, GloVe and FastText. A novel evaluation paradigm was devised for the generation of embeddings to assess their effectiveness, using the original embeddings as ground truths. Transferability across other target languages of the proposed model was assessed via pre-trained Word2Vec embeddings from Hindi and Chinese languages. We empirically prove that with a bilingual dictionary of a thousand words and a corresponding small monolingual target (Tamil) corpus, useful embeddings can be generated by transfer learning from a well-trained source (English) embedding. Furthermore, we demonstrate the usability of generated target embeddings in a few NLP use-case tasks, such as text summarization, part-of-speech (POS) tagging, and bilingual dictionary induction (BDI), bearing in mind that those are not the only possible applications. Full article
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