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Applied Sciences
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Recent Developments in Creative Language Processing
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Recent Developments in Creative Language Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Acoustics and Vibrations".

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 15025

Special Issue Editors

Prof. Dr. Michal Ptaszynski

E-Mail Website
Guest Editor
Department of Computer Science, Kitami Institute of Technology, 165 Koen-cho, Kitami 090-8507, Japan
Interests: Artificial IntelligenceNatural Language ProcessingAffect AnalysisCyberbullying DetectionAinu Language Processing
Special Issues, Collections and Topics in MDPI journals
Dr. Rafal Rzepka

E-Mail Website
Guest Editor
School of Information Science and Technology, Hokkaido University, Hokkaido 060-0808, Japan
Interests: natural language processing; common sense knowledge retrieval; dialogue processing; artificial general intelligence; affect and sentiment analysis; machine ethics
Special Issues, Collections and Topics in MDPI journals
Dr. Pawel Dybala

E-Mail Website
Guest Editor
Institute of the Middle and Far East, Faculty of International and Political Studies, Jagiellonian University, 30-063 Krakow, Poland
Interests: natural language processing; dialogue processing; humor processing; HCI; information retrieval
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Motivation for the Special Issue

In recent years, fields of natural language processing (NLP) and computational linguistics (CL) have come into stagnation. Within a massive number of papers published in those fields, only a small number present methods that are truly creative and ground-breaking, or analyze deeper and more sophisticated aspects of language such as metaphors, metonymy, irony, or other figurative uses of language, and their recent applications, for example, in an overwhelming flood of online slang, harassment, cyberbullying, or fake news.

On the other hand, the need for the research dealing with the creative use of language begins to grow exponentially, giving birth to scientific sub-fields such as the science of emoticons, automatic cyberbullying detection, or humor processing. This proves that researchers are evolving from imitative research focused on optimizing the parameters of machine learning classifiers into the application of previously developed methods to actual deep and profound studies on language phenomena. This brought us to the decision to propose this Special Issue for studies addressing such up-to-date and crucial topics. Below we describe the proposal for the Special Issue on Recent Developments in Creative Language Processing.

In general, the Special Issue (SI) will focus on two kinds of research. Firstly, processing of creative language phenomena (defined semantically as: [[Creative Language] Processing]), such as those mentioned above, and others (explained in more detail below). Secondly, the SI will also address creative methods for the processing of language (defined semantically as: [Creative [Language Processing]]).

Recent advancements in the fields of NLP and CL show a stagnation, and a lack of creativity, with the same methods being applied to similar problems, and thus resulting in publication of a multitude of overlapping and redundant publications. With the proposed SI we plan to strictly reject such papers. In particular, the scope of the SI does not include research focused on well-established topics such as miniscule improvements of part-of-speech tagging, or parameter optimization of a machine learning algorithm applied in sentiment analysis, as well as other non-creative methods for the processing of language in general. This will give room for novel and creative research that is so greatly needed in the present post-truth reality.

Aims and Scope

The SI will focus on topics deepening the knowledge on the creative use of language. Instead of taking up basic topics from the fields of CL and NLP such as the improvement of part-of-speech tagging, we will promote research focused on such creative topics as humor processing, deceptive language processing, or figurative language processing, for which the generally perceived state-of-the-art has not yet been established.

Target Audience

The SI is geared towards the audience of scientists, researchers, scholars, and students performing research in the analysis or generation of language, with a specific focus on studies about the creative use of language and the creative methods for the processing of language. The Special Issue will not accept research on basic topics for which the field has been well established, such as improvement of part-of-speech tagging, etc., unless they directly contribute to the idea of creative processing of language phenomena.

Size of the Target Market

Recent major ACL conferences host an increasingly growing number of authors. For example, for the 56th Annual Meeting of the Association for Computational Linguistics, which took place on 15–20 July 2018 in Melbourne, Australia, there was an overall number of 427 papers, with a total of 1318 authors. The acceptance rate was 24.9%. Even if there were authors who submitted more than one paper, this suggests that there is presently roughly over 5000 specialists performing studies in the areas of CL and NLP. Although not all of them at present perform specifically the research on the creative use of language, the hope is that the proposed SI will help those who are still performing non-creative studies to engage in more valuable and groundbreaking research.

Prof. Dr. Michal Ptaszynski
Prof. Rafal Rzepka
Dr. Pawel Dybala
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. Applied Sciences 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
  • Computational linguistics
  • Creative language processing
  • Figurative language processing
  • NLP applications
  • Natural language generation
  • Emotional language processing
  • Humor and joke processing
  • Deceptive language detection
  • Emoticon processing
  • Automatic cyberbullying detection
  • Fake news detection
  • Abusive language online
  • Story generation
  • Poetry generation

Published Papers (6 papers)

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Research

15 pages, 3507 KiB  
Article
Automatic Generation and Evaluation of Chinese Classical Poetry with Attention-Based Deep Neural Network
by Jianli Zhao and Hyo Jong Lee
Appl. Sci. 2022, 12(13), 6497; https://0-doi-org.brum.beds.ac.uk/10.3390/app12136497 - 27 Jun 2022
Cited by 2 | Viewed by 2564
Abstract
The computer generation of poetry has been studied for more than a decade. Generating poetry on a human level is still a great challenge for the computer-generation process. We present a novel Transformer-XL based on a classical Chinese poetry model that employs a [...] Read more.
The computer generation of poetry has been studied for more than a decade. Generating poetry on a human level is still a great challenge for the computer-generation process. We present a novel Transformer-XL based on a classical Chinese poetry model that employs a multi-head self-attention mechanism to capture the deeper multiple relationships among Chinese characters. Furthermore, we utilized the segment-level recurrence mechanism to learn longer-term dependency and overcome the context fragmentation problem. To automatically assess the quality of the generated poems, we also built a novel automatic evaluation model that contains a BERT-based module for checking the fluency of sentences and a tone-checker module to evaluate the tone pattern of poems. The poems generated using our model obtained an average score of 9.7 for fluency and 10.0 for tone pattern. Moreover, we visualized the attention mechanism, and it showed that our model learned the tone-pattern rules. All experiment results demonstrate that our poetry generation model can generate high-quality poems. Full article
(This article belongs to the Special Issue Recent Developments in Creative Language Processing)
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24 pages, 5060 KiB  
Article
A Method of Supplementing Reviews to Less-Known Tourist Spots Using Geotagged Tweets
by Victor Silaa, Fumito Masui and Michal Ptaszynski
Appl. Sci. 2022, 12(5), 2321; https://0-doi-org.brum.beds.ac.uk/10.3390/app12052321 - 23 Feb 2022
Cited by 2 | Viewed by 1665
Abstract
When planning a travel or an adventure, sightseers increasingly rely on opinions posted on the Internet tourism related websites, such as TripAdvisor, Booking.com or Expedia. Unfortunately, beautiful, yet less-known places and rarely visited sightspots often do not accumulate sufficient number of valuable opinions [...] Read more.
When planning a travel or an adventure, sightseers increasingly rely on opinions posted on the Internet tourism related websites, such as TripAdvisor, Booking.com or Expedia. Unfortunately, beautiful, yet less-known places and rarely visited sightspots often do not accumulate sufficient number of valuable opinions on such websites. On the other hand, users often post their opinions on casual social media services, such as Facebook, Instagram or Twitter. Therefore, in this study, we develop a system for supplementing insufficient number of Internet opinions available for sightspots with tweets containing opinions of such sightspots, with a specific focus on wildlife sightspots. To do that, we develop an approach consisting of a system (PSRS) for wildlife sightspots and propose a method for verifying collected geotagged tweets and using them as on-spot reviews. Tweets that contain geolocation information are considered geotagged and therefore treated as possible tourist on-spot reviews. The main challenge, however, is to confirm the authenticity of the extracted tweets. Our method includes the use of location clustering and classification techniques. Specifically, extracted geotagged tweets are clustered by using location information and then annotated taking into consideration specific features applied to machine learning-based classification techniques. As for the machine learning (ML) algorithms, we adopt a fine-tuned transformer neural network-based BERT model which implements the information of token context orientation. The BERT model achieved a higher F-score of 0.936, suggesting that applying a state-of-the-art deep learning-based approach had a significant impact on solving this task. The extracted tweets and annotated scores are then mapped on the designed Park Supplementary Review System (PSRS) as supplementary reviews for travelers seeking additional information about the related sightseeing spots. Full article
(This article belongs to the Special Issue Recent Developments in Creative Language Processing)
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24 pages, 1775 KiB  
Article
MIss RoBERTa WiLDe: Metaphor Identification Using Masked Language Model with Wiktionary Lexical Definitions
by Mateusz Babieno, Masashi Takeshita, Dusan Radisavljevic, Rafal Rzepka and Kenji Araki
Appl. Sci. 2022, 12(4), 2081; https://0-doi-org.brum.beds.ac.uk/10.3390/app12042081 - 17 Feb 2022
Cited by 7 | Viewed by 3426
Abstract
Recent years have brought an unprecedented and rapid development in the field of Natural Language Processing. To a large degree this is due to the emergence of modern language models like GPT-3 (Generative Pre-trained Transformer 3), XLNet, and BERT (Bidirectional Encoder Representations from [...] Read more.
Recent years have brought an unprecedented and rapid development in the field of Natural Language Processing. To a large degree this is due to the emergence of modern language models like GPT-3 (Generative Pre-trained Transformer 3), XLNet, and BERT (Bidirectional Encoder Representations from Transformers), which are pre-trained on a large amount of unlabeled data. These powerful models can be further used in the tasks that have traditionally been suffering from a lack of material that could be used for training. Metaphor identification task, which is aimed at automatic recognition of figurative language, is one of such tasks. The metaphorical use of words can be detected by comparing their contextual and basic meanings. In this work, we deliver the evidence that fully automatically collected dictionary definitions can be used as the optimal medium for retrieving the non-figurative word senses, which consequently may help improve the performance of the algorithms used in metaphor detection task. As the source of the lexical information, we use the openly available Wiktionary. Our method can be applied without changes to any other dataset designed for token-level metaphor detection given it is binary labeled. In the set of experiments, our proposed method (MIss RoBERTa WiLDe) outperforms or performs similarly well as the competing models on several datasets commonly chosen in the research on metaphor processing. Full article
(This article belongs to the Special Issue Recent Developments in Creative Language Processing)
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14 pages, 396 KiB  
Article
Figurative Language in Atypical Contexts: Searching for Creativity in Narco Language
by Antonio Reyes and Rafael Saldívar
Appl. Sci. 2022, 12(3), 1642; https://0-doi-org.brum.beds.ac.uk/10.3390/app12031642 - 04 Feb 2022
Cited by 1 | Viewed by 1962
Abstract
Literal language is commonly defined in terms of direct meaning, i.e., any literal utterance must convey a unique meaning. Such meaning has to be the one conventionally accepted to guarantee a successful communication. Figurative language, on the other hand, could be regarded as [...] Read more.
Literal language is commonly defined in terms of direct meaning, i.e., any literal utterance must convey a unique meaning. Such meaning has to be the one conventionally accepted to guarantee a successful communication. Figurative language, on the other hand, could be regarded as the opposite of literal language. Thus, whereas the latter is assumed to communicate a direct and explicit meaning, figurative language is related to the communication of veiled or implicit meanings. For instance, the word pozolero (stewmaker), which literally refers to a person who cooks a traditional Mexican food, when it is used in a figurative utterance, it can refer to different concepts, which are hardly related to food. Therefore, it can work instead of hitman, murderer, drug dealer, and others, in such a way its literal meaning is intentionally deviated in favor of secondary interpretations. In this regard, we are focused on analyzing the use of figurative language in an atypical context: drug trafficking. To this end, a corpus about narco language in Spanish was built. This corpus was used to train a word embedding model to identify creative ways to name narco-related concepts. The results show that various concepts are commonly expressed through figurative devices, such as metaphor, metonymy, or mental imagery. This fact corroborates that figurative language is quite recurrent in our daily communication, regardless of the context. In addition, we show how this creativity can be recognized by applying Natural Language Processing (NLP) techniques. Full article
(This article belongs to the Special Issue Recent Developments in Creative Language Processing)
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12 pages, 2286 KiB  
Article
Developing Language-Specific Models Using a Neural Architecture Search
by YongSuk Yoo and Kang-moon Park
Appl. Sci. 2021, 11(21), 10324; https://0-doi-org.brum.beds.ac.uk/10.3390/app112110324 - 03 Nov 2021
Cited by 1 | Viewed by 1151
Abstract
This paper applies the neural architecture search (NAS) method to Korean and English grammaticality judgment tasks. Based on the previous research, which only discusses the application of NAS on a Korean dataset, we extend the method to English grammatical tasks and compare the [...] Read more.
This paper applies the neural architecture search (NAS) method to Korean and English grammaticality judgment tasks. Based on the previous research, which only discusses the application of NAS on a Korean dataset, we extend the method to English grammatical tasks and compare the resulting two architectures from Korean and English. Since complex syntactic operations exist beneath the word order that is computed, the two different resulting architectures out of the automated NAS language modeling provide an interesting testbed for future research. To the extent of our knowledge, the methodology adopted here has not been tested in the literature. Crucially, the resulting structure of the NAS application shows an unexpected design for human experts. Furthermore, NAS has generated different models for Korean and English, which have different syntactic operations. Full article
(This article belongs to the Special Issue Recent Developments in Creative Language Processing)
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28 pages, 4630 KiB  
Article
Deep Learning for Information Triage on Twitter
by Michal Ptaszynski, Fumito Masui, Yuuto Fukushima, Yuuto Oikawa, Hiroshi Hayakawa, Yasunori Miyamori, Kiyoshi Takahashi and Shunzo Kawajiri
Appl. Sci. 2021, 11(14), 6340; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146340 - 08 Jul 2021
Cited by 6 | Viewed by 2300
Abstract
In this paper, we present a Deep Learning-based system for the support of information triaging on Twitter during emergency situations, such as disasters, or other influential events, such as political elections. The system is based on the assumption that a different type of [...] Read more.
In this paper, we present a Deep Learning-based system for the support of information triaging on Twitter during emergency situations, such as disasters, or other influential events, such as political elections. The system is based on the assumption that a different type of information is required right after the event and some time after the event occurs. In a preliminary study, we analyze the language behavior of Twitter users during two kinds of influential events, namely, natural disasters and political elections. In the study, we analyze the credibility of information included by users in tweets in the above-mentioned situations, by classifying the information into two kinds: Primary Information (first-hand reports) and Secondary Information (second-hand reports, retweets, etc.). We also perform sentiment analysis of the data to check user attitudes toward the occurring events. Next, we present the structure of the system and compare a number of classifiers, including the proposed one based on Convolutional Neural Networks. Finally, we validate the system by performing an in-depth analysis of information obtained after a number of additional events, including an eruption of a Japanese volcano Ontake on 27 September 2014, as well as heavy rains and typhoons that occurred in 2020. We confirm that the methods works sufficiently well even when trained on data from nearly 10 years ago, which strongly suggests that the model is well-generalized and sufficiently grasps important aspects of each type of classified information. Full article
(This article belongs to the Special Issue Recent Developments in Creative Language Processing)
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