Language Research Articles

Jacobson’s Distinctive Features and the Analysis of assimilation in Kurdish Bahdini local dialect

Distinctive Features are generally used to describe the smallest units of sound in both phonetic and phonological analyses which aims to differentiate between the meaning of one word and another in a certain language. The topic of distinctive features has always caught the interest of phonologists and many schoolars used these features as the smallest units of sounds in order to describe sound features in all human languages. This research is an attempt to represent the distinctive features of Kurdish language, as well as to explain assimilation through Jacobson,s Distinctive Features in order to reach some phonological rules in Kurdish language. Samples are taken and analysed from revels written by speakers from the center of Duhok in order to represent the distinctive features of the sounds which affect the adjacent sounds, as well as creating the rules of the sounds which affect each other. Jacobson’s features are mainly based on a acoustic description of sounds. The most important hypotheses of this research show that this theory meets the following requisitions: Distinctive Features (DFs) can describe a natural class of sounds. They can also describe the distinctions of sounds in all languages of the world and these features can be defined phonological. In conolusion it is clear that in Bahdini local dialect, The changes often affect the sound which carries the feature (±voiced) of stop and fricative sounds in general, and upon the feature (+voiced) of fricative in novel particularly.

Interlingual Syntactic Parsing: An Optimized Head-Driven Parsing for English to Indian Language Machine Translation

In the era of Artificial Intelligence (AI), significant progress has been made by enabling machines to understand and communicate in human languages. Central to this progress are parsers, which play a vital role in syntactic analysis and support various Natural language Processing (NLP) applications, including Machine Translation and sentiment analysis. This paper introduces a robust implementation of an optimized Head-Driven Parser designed to advance NLP capabilities beyond the limitations of traditional Lexicalized Tree Adjoining Grammar (L-TAG) based Parser. Traditional parser, while effective, often struggle with the capturing complexities of natural languages, especially translation between English to Indian languages. By leveraging Bi-directional approach and Head-Driven techniques, this research offers a revolutionary enhancement in parsing frameworks. This method not only improves performance in syntactic analysis but also facilitates complex tasks such as discourse analysis and semantic parsing. This research involves experimentation the Bi-Directional Parser on a dataset of 15,000 sentences, resulting a reduction in derivation variations compared to conventional TAG Parsers. This advancement highlights how Head-Driven Parsing can overcome traditional constraints and provide more reliable linguistic analysis. The paper demonstrates how this new implementation not only builds on the strengths of L-TAG but also addresses its limitations and contributes to expanding the scope of Tree Adjoining Grammarbased methodologies and advancing the field of Machine Translation.

Impacto de la inteligencia artificial en la moda. Análisis de influencers digitales en las fashion weeks internacionales

The phenomenon of digital influencers has transformed the way brands communicate with their audience via social networks. In a society that is governed by immediacy, companies have taken the lead in considering Instagram as the platform on which to advertise their products. In this scenario, influencers become a key tool and a business market for advertising. In parallel, Artificial Intelligence has given riseto a world of avatars, a non-human profile, that develop capabilities with unknown effects on audiencesand that focus the attention of fashion brands. The objective of this research is to understand how digital influencers communicate on Instagram and the impact on luxury brands. A dual-focus content analysis methodology (quantitative/qualitative) and SPSS statistical tool are applied. The first results show the level of frequency of digital influencers in the advertising campaigns of fashion brands, the presence of luxury brands in their conversations and a positive interaction with users, despite being profiles thatcannot reproduce the emotions of human language, we have been able to obtain some results whichsupport that, although the avatars generated by artificial intelligence (AI) have the necessary characteristics to play a relevant role in brand advertising on social networks, it cannot be said that are involved in the “influencer” profession in at least not in the near future. In the future, it may be equivalent to the work done by humans.

Transcranial photobiomodulation on the left inferior frontal gyrus enhances Mandarin Chinese L1 and L2 complex sentence processing performances

This study investigated the causal enhancing effect of transcranial photobiomodulation (tPBM) over the left inferior frontal gyrus (LIFG) on syntactically complex Mandarin Chinese first language (L1) and second language (L2) sentence processing performances. Two (L1 and L2) groups of participants (thirty per group) were recruited to receive the double-blind, sham-controlled tPBM intervention via LIFG, followed by the sentence processing, the verbal working memory (WM), and the visual WM tasks. Results revealed a consistent pattern for both groups: (a) tPBM enhanced sentence processing performance but not verbal WM for linear processing of unstructured sequences and visual WM performances; (b) Participants with lower sentence processing performances under sham tPBM benefited more from active tPBM. Taken together, the current study substantiated that tPBM enhanced L1 and L2 sentence processing, and would serve as a promising and cost-effective noninvasive brain stimulation (NIBS) tool for future applications on upregulating the human language faculty.

Leveraging AI automated emergency response with natural language processing: Enhancing real-time decision making and communication

Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies play an essential role in the ongoing development and advancements of emergency response systems. This paper explores the new opportunities and possibilities offered by the incorporation of AI-driven automated emergency response systems, and the benefits of using NLP to increase the efficiency of human language processing and communication in real time. Using case studies and applications, we analyse how integrating AI and NLP technologies can reduce response times, improve situational awareness and coordination between emergency services. We then explore how AI-driven models can be used to predict natural disasters, optimise emergency vehicle routes and identify areas of need in real time through the analysis of social media and emergency communications via NLP. However, we also discuss the issues that come with implementing these technologies, such as data-privacy challenges, technical limitations and the need for sustained retraining of AI systems. The conclusions of this research indicate that continued collaboration between stakeholders is necessary to overcome these barriers, and that substantial innovation is needed to maximise the potential of AI and NLP technologies in emergency management.

From Optimism to Concern: Unveiling Sentiments and Perceptions Surrounding ChatGPT on Twitter

Artificial intelligence (AI) technologies, as a product of processes aimed at imitating human intelligence through computers and software, affect our daily lives in various aspects, including cultural, technological, and economic. The advent of ChatGPT, developed by OpenAI, signifies a pivotal AI advancement in human language comprehension and generation, heralding profound implications across societal, economic, and cultural dimensions. However, a notable gap exists in scholarly literature concerning the examination of perceptions and discussions surrounding ChatGPT. This study aims to address this gap by analyzing Twitter conversations, comprising 1.1 million tweets containing “ChatGPT” or “#ChatGPT” between December 1 2022 and June 1 2023, with geolocation data. Employing a text as data approach encompassing text, sentiment, and semantic network analyses, sentiment polarity and lexical patterns were explained, while semantic network analysis revealed central expressions and prominent themes in Twitter discussions. The study highlights the social effects of AI technologies from the perspective of Twitter users and reveals the sentimental tendency and themes in tweets with geographical and economic dimensions. The findings reflect the prevalence of positive sentiment and hype toward ChatGPT while there are also concerns regarding privacy, cybersecurity, and misuse of AI tools. More importantly, content generation, technical aspects, business applications, educational use, and competitors are among the main themes of ChatGPT-related discussions on Twitter.

The Role of L1 and Age Effect in the Acquisition of EnglishArticles among Croatian Young EFL Learners

The category of definiteness/indefiniteness is often seen as a linguistic universal (Chomsky 2000; Silić 2000) that exists in every human language but is expressed with different linguistic means and patterns in different languages. In English, this category is grammaticalized primarily via articles (Zergollern-Miletić 2008). The Croatian language does not contain an article system, and acquiring English articles represents a major problem for most learners of English as a foreign language (Balenović and Medved Krajnović 2013; Zergollern-Miletić 2015). The present paper investigates difficulties in acquiring English articles among young learners in Croatia in written tasks. Pupils involved in the research started learning English in the first grade of primary school. A total of 76 primary school learners from six different classes (schools) and different age groups were included in this research. The initial hypothesis is that the correct use of articles increases as learners’ overall L2 proficiency increases due to exposure to instructed EFL learning. A qualitative analysis was carried out using error analysis, i.e., proper/wrong use, or omission of the article. The findings partially confirmed the initial hypothesis and showed that our participants were becoming aware of the existence of English articles (they comprehended that articles should precede nouns), but we found very frequent article omission, which, according to our research results, indicates that the process of developing and understanding the semantic and pragmatic role of English articles is long and fluctuating specifically with learners whose L1 lacks the article system.

Modeling of psychological time cognition with Human Language based Consciousness model

Modeling of psychological time cognition with Human Language based Consciousness model

Challenges of Integrating ICD 11 into Automatic Alerting Systems.

Automatic alerting systems (AASs) can identify adverse health events but emergency communication relies on human operators and natural languages. For complete automation, we need to code the diversity of adverse events in a granularity that supports optimal dispatches. Hence, AAs shall integrate with the International Classification of Diseases (ICD). The ICD-11 coding system includes chapters for external causes of injury. However, ICD-11 supports coding injury incidents in electronic health records (EHRs) after they have occurred, while disregarding integrating real-time injury reporting within its framework. We explore the potential challenges associated with integrating ICD-11 into AAS by analyzing external causes of morbidity or mortality and the dimensions of external causes as potential areas of integration. We recognize the themes: (i) incident of injury, (ii) mode of transport, (iii) indoor location, (iv) outdoor location, and (v) type of building, and identify four challenges: (i) conceptual differences between the two systems, (ii) injury identification, (iii) presence of entities below the shoreline in ICD-11, and (iv) lack of specificity in certain ICD-11 codes related to AASs. For easy integration of ICD-11 into AASs, we recommend an AAS data dictionary and propose ICD-11 updates related to external causes of injury.

Large models of what? Mistaking engineering achievements for human linguistic agency

In this paper we argue that key, often sensational and misleading, claims regarding linguistic capabilities of Large Language Models (LLMs) are based on at least two unfounded assumptions: the assumption of language completeness and the assumption of data completeness. Language completeness assumes that a distinct and complete thing such as “a natural language” exists, the essential characteristics of which can be effectively and comprehensively modelled by an LLM. The assumption of data completeness relies on the belief that a language can be quantified and wholly captured by data. Work within the enactive approach to cognitive science makes clear that, rather than a distinct and complete thing, language is a means or way of acting. Languaging is not the kind of thing that can admit of a complete or comprehensive modelling. From an enactive perspective we identify three key characteristics of enacted language; embodiment, participation, and precariousness, that are absent in LLMs, and likely incompatible in principle with current architectures. We argue that these absences imply that LLMs are not now and cannot in their present form be linguistic agents the way humans are. We illustrate the point in particular through the phenomenon of “algospeak”, a recently described pattern of high-stakes human language activity in heavily controlled online environments. On the basis of these points, we conclude that sensational and misleading claims about LLM agency and capabilities emerge from a deep misconception of both what human language is and what LLMs are.

A tutorial: self-created film as a semiotic resource in AAC

Sharing personal stories with others is essential to human interaction and language development. To communicate, individuals use a variety of semiotic resources, including images, symbols, and written and spoken language. These modes are deployed in the co-construction of a daily face-to-face conversation. A self-created film can serve as a valuable resource to facilitate a deeper understanding of a personal experience, especially where spoken or written language may present a challenge, for example, for people who rely on augmentative and alternative communication (AAC). Although the AAC literature indicates that using videos delivers benefits for aided communicators, guidelines on how to self-create, use, and transcribe them are rare. The present paper, a tutorial, describes how people who use AAC can develop a personal-video-scene (PVS) via the Film as Observable Communication (FaOC) method to utilize self-created films in sharing their stories. The first part of this paper, the theoretical framework, describes theories, methods, and practices from the fields of AAC, social semiotics, and visual anthropology, on which the FaOC method is based. The second part provides a step-by-step tutorial delivering practical guidance on how to create, use, and transcribe the PVS as a resource in conversations.

A Literature Review on Emotional Intelligence of Large Language Models (LLMs)

Large Language Models(LLMs) are artificial intelligence models that use deep neural networks to perform Natural Language Processing (NLP) tasks. These tasks include interaction between humans and computers, enabling computers to interpret and generate human languages in a meaningful manner. Large Language models are called "large" because of the architecture’s size and the huge sets of training text data. With the emergence of transformer-based LLMs, the game of NLPs has reached another level. This is due to their ability to handle long-range text dependencies in parallel. The growing prevalence of transformer-based LLMs in human lives has necessitated evaluating the scope of the Emotional Intelligence(EI) of LLMs. This paper will discuss the need for emotional intelligence in transformer-based LLMs and the various existing studies that have evaluated this aspect. The potential challenges of the LLMs along with the future directions for research in this field will also be discussed.

Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning.

The 3' untranslated regions (3'UTRs) of messenger RNAs contain many important cis-regulatory elements that are under functional and evolutionary constraints. It is hypothesized that these constraints are similar to grammars and syntaxes in human languages and can be modeled by advanced natural language techniques such as Transformers, which has been very effective in modeling complex protein sequence and structures. Here 3UTRBERT is described, which implements an attention-based language model, i.e., Bidirectional Encoder Representations from Transformers (BERT). 3UTRBERT is pre-trained on aggregated 3'UTR sequences of human mRNAs in a task-agnostic manner; the pre-trained model is then fine-tuned for specific downstream tasks such as identifying RBP binding sites, m6A RNA modification sites, and predicting RNA sub-cellular localizations. Benchmark results show that 3UTRBERT generally outperformed other contemporary methods in each of these tasks. More importantly, the self-attention mechanism within 3UTRBERT allows direct visualization of the semantic relationship between sequence elements and effectively identifies regions with important regulatory potential. It is expected that 3UTRBERT model can serve as the foundational tool to analyze various sequence labeling tasks within the 3'UTR fields, thus enhancing the decipherability of post-transcriptional regulatorymechanisms.

Even laypeople use legalese

Whereas principles of communicative efficiency and legal doctrine dictate that laws be comprehensible to the common world, empirical evidence suggests legal documents are largely incomprehensible to lawyers and laypeople alike. Here, a corpus analysis (n = 59) million words) first replicated and extended prior work revealing laws to contain strikingly higher rates of complex syntactic structures relative to six baseline genres of English. Next, two preregistered text generation experiments (n = 286) tested two leading hypotheses regarding how these complex structures enter into legal documents in the first place. In line with the magic spell hypothesis, we found people tasked with writing official laws wrote in a more convoluted manner than when tasked with writing unofficial legal texts of equivalent conceptual complexity. Contrary to the copy-and-edit hypothesis, we did not find evidence that people editing a legal document wrote in a more convoluted manner than when writing the same document from scratch. From a cognitive perspective, these results suggest law to be a rare exception to the general tendency in human language toward communicative efficiency. In particular, these findings indicate law's complexity to be derived from its performativity, whereby low-frequency structures may be inserted to signal law's authoritative, world-state-altering nature, at the cost of increased processing demands on readers. From a law and policy perspective, these results suggest that the tension between the ubiquity and impenetrability of the law is not an inherent one, and that laws can be simplified without a loss or distortion of communicative content.

Natural language processing in dermatology: A systematic literature review and state of the art.

Natural Language Processing (NLP) is a field of both computational linguistics and artificial intelligence (AI) dedicated to analysis and interpretation of human language. This systematic review aims at exploring all the possible applications of NLP techniques in the dermatological setting. Extensive search on 'natural language processing' and 'dermatology' was performed on MEDLINE and Scopus electronic databases. Only journal articles with full text electronically available and English translation were considered. The PICO (Population, Intervention or exposure, Comparison, Outcome) algorithm was applied to our study protocol. Natural Language Processing (NLP) techniques have been utilized across various dermatological domains, including atopic dermatitis, acne/rosacea, skin infections, non-melanoma skin cancers (NMSCs), melanoma and skincare. There is versatility of NLP in data extraction from diverse sources such as electronic health records (EHRs), social media platforms and online forums. We found extensive utilization of NLP techniques across diverse dermatological domains, showcasing its potential in extracting valuable insights from various sources and informing diagnosis, treatment optimization, patient preferences and unmet needs in dermatological research and clinical practice. While NLP shows promise in enhancing dermatological research and clinical practice, challenges such as data quality, ambiguity, lack of standardization and privacy concerns necessitate careful consideration. Collaborative efforts between dermatologists, data scientists and ethicists are essential for addressing these challenges and maximizing the potential of NLP in dermatology.

The Acquisition of de: Investigating Recursive Possession in Mandarin-Speaking Kids

Recursive grammar is the sole cognitive mechanism unique to human languages. This study investigates three aspects of the acquisition of the recursive possessive marker de by Mandarin-speaking children: the feasibility of recursive possessive (RP) levels, performance in both cognition and competence, and the age at which these abilities emerge. Following a development theory of recursive possessives in language acquisition, we conducted two tasks—comprehension and production—each comprising 12 questions. The comprehension task involved a truth-value judgment, while the production task consisted of a question-and-answer session. Forty-eight preschoolers, aged three to five, were recruited from a kindergarten and divided into three age groups. Additionally, 16 adults were randomly recruited to serve as a baseline for comparison. The results revealed that significant effects of levels of recursive possessiveness were observed only in five-year-old Mandarin-speaking children, not in their three- and four-year-old counterparts. The comprehension task was significantly easier for all participants compared to the production task, suggesting that understanding the material was not challenging for any group, with no substantial differences noted among them. The results demonstrated that age effects were critical for younger children in adopting RPs. Specifically, three- and four-year-old Mandarin-speaking children demonstrated the ability to comprehend multiple levels of RP but struggled to produce them. In contrast, five-year-olds exhibited adult-like comprehension and the ability to produce Level 1 RP.

Language in Brains, Minds, and Machines.

It has long been argued that only humans could produce and understand language. But now, for the first time, artificial language models (LMs) achieve this feat. Here we survey the new purchase LMs are providing on the question of how language is implemented in the brain. We discuss why, a priori, LMs might be expected to share similarities with the human language system. We then summarize evidence that LMs represent linguistic information similarly enough to humans to enable relatively accurate brain encoding and decoding during language processing. Finally, we examine which LM properties-their architecture, task performance, or training-are critical for capturing human neural responses to language and review studies using LMs as in silico model organisms for testing hypotheses about language. These ongoing investigations bring us closer to understanding the representations and processes that underlie our ability to comprehend sentences and express thoughts in language.

A transformer-based spelling error correction framework for Bangla and resource scarce Indic languages

Spelling error correction is the task of identifying and rectifying misspelled words in texts. It is a potential and active research topic in Natural Language Processing because of numerous applications in human language understanding. The phonetically or visually similar yet semantically distinct characters make it an arduous task in any language. Earlier efforts on spelling error correction in Bangla and resource-scarce Indic languages focused on rule-based, statistical, and machine learning-based methods which we found rather inefficient. In particular, machine learning-based approaches, which exhibit superior performance to rule-based and statistical methods, are ineffective as they correct each character regardless of its appropriateness. In this paper, we propose a novel detector-purificator-corrector framework, DPCSpell based on denoising transformers by addressing previous issues. In addition to that, we present a method for large-scale corpus creation from scratch which in turn resolves the resource limitation problem of any left-to-right scripted language. The empirical outcomes demonstrate the effectiveness of our approach, which outperforms previous state-of-the-art methods by attaining an exact match (EM) score of 94.78%, a precision score of 0.9487, a recall score of 0.9478, an f1 score of 0.948, an f0.5 score of 0.9483, and a modified accuracy (MA) score of 95.16% for Bangla spelling error correction. The models and corpus are publicly available at https://tinyurl.com/DPCSpell.

Natural Language Processing Pretraining Language Model for Computer Intelligent Recognition Technology

Computer intelligent recognition technology refers to the use of computer vision, Natural Language Processing (NLP), machine learning and other technologies to enable computers to recognize, analyze, understand and answer human language and behavior. The common applications of computer intelligent recognition technology include image recognition, NLP, face recognition, target tracking, and other fields. NLP is a field of computer science, which involves the interaction between computers and natural languages. NLP technology can be used to process, analyze and generate natural language data, such as text, voice and image. Common NLP technology applications include language translation, emotion analysis, text classification, speech recognition and question answering system. Language model is a machine learning model, which uses a large number of text data for training to learn language patterns and relationships in text data. Although the language model has made great progress in the past few years, it still faces some challenges, including: poor semantic understanding, confusion in multilingual processing, slow language processing and other shortcomings. Therefore, in order to optimize these shortcomings, this article would study the pre-training language model based on NLP technology, which aimed at using NLP technology to optimize and improve the performance of the language model, thus optimizing the computer intelligent recognition technology. The model had a higher language understanding ability and more accurate prediction ability. In addition, the model could learn language rules and structures by using a large number of corpus, so as to better understand natural language. Through experiments, it could be known that the data size and total computing time of the traditional Generative Pretrained Transformer-2 (GPT-2) language model were 10 GB and 97 hours respectively. The data size and total computing time of BERT (Bidirectional Encoder Representations from Transformer) were 12 GB and 86 hours respectively. The data size and total computing time of the pre-training language model based on NLP were 18 GB and 71 hours respectively. Obviously, the pre-training language model based on NLP had a larger data size and shorter computing time. The experimental results showed that the NLP technology could better optimize the language model and effectively improve its various capabilities. This article opened up a new development direction for computer intelligent recognition technology and provided excellent technical support for the development of language models.

Linguistic Trends and Foreign Language Teaching: The Case of French

Linguistics is generally defined as the scientific study of human language. Whereas language is considered as an instrument of communication through which human experience is analyzed differently. Meanwhile, the spread of linguistics theories is propelled by the desire to find better ways to learn and acquire foreign languages, such as French. Hence, in what ways have the development of linguistics theories contributed to language learning and acquisition? An attempt to answer this question was made and some linguistic theories were explained.