Special Issue "Current Trends in Natural Language Processing (NLP) and Human Language Technology (HLT)"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 23 April 2023 | Viewed by 697

Special Issue Editor

Prof. Dr. Florentina Hristea
E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest, 010014 Bucharest, Romania
Interests: artificial intelligence (AI); knowledge representation; natural language processing; computational linguistics; human language technology; computational statistics applied in natural language processing; data analysis
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Special Issue Information

Dear Colleagues,

This Special Issue is concerned with technologies for processing human language in the form of text, with Natural Language Processing (NLP) tools and techniques ultimately responding to the two main existing challenges: natural language understanding and natural language generation. In the broad spectrum of research areas that are concerned with computational approaches to natural language, we will be looking at all main levels at which language processing is performed: the morphological level, the syntactic level, the semantic level, the pragmatic level, both from a theoretical and from a practical point of view.

AI-powered text processing continues to represent a strong trend in artificial intelligence (AI) primarily due to the genuine explosion of texts on the World Wide Web. NLP is one of the most important technologies in use today, especially due to the large and growing amount of online text, which needs to be understood in order for its enormous value to be fully asserted. NLP can make sense of the unstructured data that are produced by social platforms and other social data sources, and can help organize them into a more structured model that supports various types of tasks and applications, which are all of great interest to this Special Issue.

The large size, unrestrictive nature, and ambiguity of natural langauge have led to the vast development of the NLP field in various ways and from different perspectives, all of which are of interest to this Special Issue. Most of the approaches can be viewed as complementary, while in recent years machine-learning methods have strongly and successfully emerged. Large annotated bodies of text (corpora) have been employed to train machine-learning algorithms and to provide gold standards for evaluation corresponding to specific tasks. However, there are still various types of modern NLP applications (e.g. hate speech detection, stance detection) for which we are just now moving towards creating an appropriate benchmarking system. We hope this Special Issue will take steps in this respect as well. Although many machine-learning models have been developed for NLP applications, recently, deep learning approaches have achieved remarkable results across many NLP tasks. This Special Issue is interested in the use and exploration of current advances in machine-learning and deep learning for NLP topics, including (but not limited to) information extraction, information retrieval and text mining, text summarization, computational social science, discourse and dialog systems, interpretability, ethics in NLP, linguistic theories and NLP for social good. 

Although NLP is not a new science, the technology emerging from this field of study is rapidly advancing, thanks to an increased interest in human–machine communication. Human language technology (HLT) has been recognized as representing a major challenge for computing, requiring advanced NLP, as well as the availability of big data, resulting in large-scale systems and applications. Knowledge of natural language processing (NLP) and computational linguistics (CL), as well as concerning many of their application-oriented aspects, is required for researching software and systems that bridge the linguistic gap between people and machines. The involved human–computer interaction enables various types of real-world applications, all of which are of interest to this Special Issue.

Prof. Dr. Florentina Hristea
Guest Editor

Manuscript Submission Information

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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. Mathematics 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.


  • natural language processing
  • computational linguistics
  • human language technology
  • human–computer interaction
  • knowledge representation
  • sentiment analysis
  • social media mining
  • machine learning
  • deep learning
  • big data

Published Papers (1 paper)

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Sentence-CROBI: A Simple Cross-Bi-Encoder-Based Neural Network Architecture for Paraphrase Identification
Mathematics 2022, 10(19), 3578; https://doi.org/10.3390/math10193578 - 30 Sep 2022
Viewed by 494
Since the rise of Transformer networks and large language models, cross-encoders have become the dominant architecture for various Natural Language Processing tasks. When dealing with sentence pairs, they can exploit the relationships between those pairs. On the other hand, bi-encoders can obtain a [...] Read more.
Since the rise of Transformer networks and large language models, cross-encoders have become the dominant architecture for various Natural Language Processing tasks. When dealing with sentence pairs, they can exploit the relationships between those pairs. On the other hand, bi-encoders can obtain a vector given a single sentence and are used in tasks such as textual similarity or information retrieval due to their low computational cost; however, their performance is inferior to that of cross-encoders. In this paper, we present Sentence-CROBI, an architecture that combines cross-encoders and bi-encoders to obtain a global representation of sentence pairs. We evaluated the proposed architecture in the paraphrase identification task using the Microsoft Research Paraphrase Corpus, the Quora Question Pairs dataset, and the PAWS-Wiki dataset. Our model obtains competitive results compared with the state-of-the-art by using model ensembles and a simple model configuration. These results demonstrate that a simple architecture that combines sentence pair and single-sentence representations without using complex pre-training or fine-tuning algorithms is a viable alternative for sentence pair tasks. Full article
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