Language Research Articles

Metaphor forces argument overtness

Abstract This paper uncovers how metaphor forces argument overtness – across languages and parts of speech. It addresses the relationship between semantically unsaturated terms, functors, and the argument terms that complete them. When the component terms’ default senses clash semantically, a metaphor arises. In such cases, the argument must be overt, in contrast to literal uses. It is possible to say Everyone was waiting at the hotel. Finally, Kim arrived. By contrast, people do not use arrived metaphorically without a goal argument: Everything had been pointing to that conclusion all along. *Finally, Kim arrived. What they say is Finally, Kim arrived at it. We illustrate the phenomenon with powerful and diverse evidence: three corpus studies (Indo-Aryan languages, British English, Vera’a) and a sentence-completion experiment with around 250 native speakers of English. Both the corpus studies and the experiment show no or almost no exceptions to metaphor-driven argument overtness. The strength of the effect contrasts with a complete lack of speaker awareness. We propose that metaphor-driven argument overtness – as well as the lack of speaker consciousness – is a universal phenomenon that can be accounted for in terms of human language processing.

Conversational agent in HCI a review

Nowadays, AI technology is developing rapidly and slowly covering all areas of life. AI facilitates different parts of our lives and provides us with a lot of help. For example, in learning, students can acquire knowledge more conveniently through AI, and buyers can find suitable goods more conveniently through AI when shopping. At the same time, more and more technology is used in the field of voice agents, which allows humans to enjoy a lot of better services. In this article, we will study to better understand "how to understand human natural language and how the repository of knowledge is built." In the article we build with examples and deep learning models (CNN and RNN) through databases. Through repeated research and analysis, we can find that there are some limitations in this paper, such as the single learning model and the insufficient elaboration of data analysis and signal system technology. But at the same time, we also found a lot of future application prospects that voice agents can develop, it can be applied in many fields, such as finance, medical care, education and so on. For example, in children's education, parents can use voice agents to set time limits and monitor their children's progress. Existing digital interactive storytelling systems have limitations in terms of available storybooks and hand-crafted issues. Voice agents are becoming more popular in everyday scenarios, and more users are adopting devices like Siri and Google Assistant. In the future, conversational agents are expected to play an important role in oral communication with users.

THE DEGREE OF NEGATIVITY OF MORPHOLOGICAL, SYNTACTIC AND LEXICAL NEGATION IN ENGLISH AS SEEN BY ENGLISH LANGUAGE AND LITERATURE STUDENTS

The phenomenon of negation is part of perception and a universal property of all human languages. The focus of this study lies on morphological, syntactic and lexical negation, with the main goal to determine how they are perceived in terms of the degree of negativity by English language and literature students (N=91). The main administered instrument was the Polarity scale, comprising utterances with the three broad types of negation. The obtained results were analysed via IBM SPSS 21. Overall, the sample evaluated the affixal–syntactic negation pairs fairly equally, as negative or mildly negative. On the aggregate scores, though, the affixal negation (Neg. pref. + Adj) was evaluated more negatively than the syntactic one (not + Adj). The participants evaluated the absolute negative never more positively than its semantics implies (as negative only), and semi-negatives, and negated frequency and quantity adverbs were rated quite evenly. In future studies, we propose the use of utterances with inanimate subjects in addition, as well as replication in different cultural settings to check whether there are culturally induced differences or else cognitive similarity.

Phenomenology and cognitive linguistics in dialogue: A review of Ortega y Gasset's theory of emotive gesture as metaphor

AbstractThe present study pursues three objectives. First, to expose and discuss the contributions of the philosopher José Ortega y Gasset to the phenomenological study of gestures and emotive gesture. Secondly, to critically review one of the central theses defended by Ortega, according to which “every expressive phenomenon”—including, therefore, the emotive gesture—involves “a transposition, that is to say, an essential metaphor.” This thesis invites us, in the third objective, to establish a dialogue between phenomenology and cognitive linguistics (as developed by Lakoff, Langacker and Talmy), assuming an experientialist position vis‐à‐vis human reason and language, in opposition to the objectivist and formalist position of Chomsky's generative linguistics. In this interdisciplinary context, we will study the phenomenon of emotive gesture, in particular the expression of the emotion of anger, as analyzed phenomenologically by Ortega y Gasset.

Optimizing Chatbot Effectiveness through Advanced Syntactic Analysis: A Comprehensive Study in Natural Language Processing

In the era of digitalization, the interaction between humans and machines, particularly in Natural Language Processing, has gained crucial importance. This study focuses on improving the effectiveness and accuracy of chatbots based on Natural Language Processing. Challenges such as the variability of human language and high user expectations are addressed, analyzing critical aspects such as grammatical structure, keywords, and contextual factors, with a particular emphasis on syntactic structure. An optimized chatbot model that considers explicit content and the user’s underlying context and intentions is proposed using machine learning techniques. This approach reveals that specific features, such as syntactic structure and keywords, are critical to the accuracy of chatbots. The results show that the proposed model adapts to different linguistic contexts and offers coherent and relevant answers in real-world situations. Furthermore, user satisfaction with this advanced model exceeds traditional models, aligning with expectations of more natural and humanized interactions. This study demonstrates the feasibility of improving chatbot–user interaction through advanced syntactic analysis. It highlights the need for continued research and development in this field to achieve significant advances in human–computer interaction.

Evolutionary continuity and divergence of auditory dorsal and ventral pathways in primates revealed by ultra-high field diffusion MRI

Auditory dorsal and ventral pathways in the human brain play important roles in supporting speech and language processing. However, the evolutionary root of the dual auditory pathways in the primate brain is unclear. By parcellating the auditory cortex of marmosets (a New World monkey species), macaques (an Old World monkey species), and humans using the same individual-based analysis method and tracking the pathways from the auditory cortex based on multi-shell diffusion-weighted MRI (dMRI), homologous auditory dorsal and ventral fiber tracks were identified in these primate species. The ventral pathway was found to be well conserved in all three primate species analyzed but extend to more anterior temporal regions in humans. In contrast, the dorsal pathway showed a divergence between monkey and human brains. First, frontal regions in the human brain have stronger connections to the higher-level auditory regions than to the lower-level auditory regions along the dorsal pathway, while frontal regions in the monkey brain show opposite connection patterns along the dorsal pathway. Second, the left lateralization of the dorsal pathway is only found in humans. Moreover, the connectivity strength of the dorsal pathway in marmosets is more similar to that of humans than macaques. These results demonstrate the continuity and divergence of the dual auditory pathways in the primate brains along the evolutionary path, suggesting that the putative neural networks supporting human speech and language processing might have emerged early in primate evolution.

An Overview of the Application of Convolutional Neural Networks inSentiment Analysis

The field of natural language processing, or NLP, uses its understanding of human language to find practical solutions to issues. It mainly includes two parts: the core task and the application. The core task represents the common problem that needs to be solved in various natural language application directions. It includes language models, morphology, grammar analysis, semantic analysis, etc. At the same time, the application section focuses on specific natural language processing tasks such as machine translation, information retrieval, question-answering systems, dialogue systems, etc. Natural language processing has made a significant contribution to the development of human society and the economy and provides strong support for all aspects of research work. Opinion mining, or sentiment analysis, is a subfield of natural language processing that develops systems for identifying and extracting ideas from text. Sentiment analysis is a hot topic since it has many practical applications. Many opinion-expressing texts are available on review sites, forums, blogs, and social media as the amount of publicly available information on the Internet grows. This unstructured information can then be automatically transformed into structured data about products, services, brands, politics, or other topics on which people can express their opinions using sentiment analysis systems. This information can be used for marketing analytics, public relations, product reviews, network sponsor ratings, product feedback, and customer service. With the rapid growth of labeled sample data sets and the notable enhancement in graphics processor (GPU) performance, convolutional neural network research has advanced rapidly and achieved remarkable leads to various computer vision tasks. By reviewing the application of CNN, we see that convolutional operations are naturally suitable for some text processing and, thus, naturally suitable for the background of sentiment analysis.

Human languages with greater information density have higher communication speed but lower conversation breadth.

Human languages vary widely in how they encode information within circumscribed semantic domains (for example, time, space, colour, human body parts and activities), but little is known about the global structure of semantic information and nothing about its relation to human communication. We first show that across a sample of ~1,000 languages, there is broad variation in how densely languages encode information into words. Second, we show that this language information density is associated with a denser configuration of semantic information. Finally, we trace the relationship between language information density and patterns of communication, showing that informationally denser languages tend towards faster communication but conceptually narrower conversations or expositions within which topics are discussed at greater depth. These results highlight an important source of variation across the human communicative channel, revealing that the structure of language shapes the nature and texture of human engagement, with consequences for human behaviour across levels of society.

Natural Language Processing Advancements: Breaking Barriers in Human-Computer Interaction

Natural Language Processing (NLP) advancements have revolutionized human-computer interaction, breaking barriers and opening new frontiers in technology. NLP techniques enable machines to understand, interpret, and generate human language, facilitating seamless communication between humans and computers. This paper explores recent advancements in NLP technology, highlighting their impact on various domains and discussing challenges and future directions in the field

Exploring the role of large language models in radiation emergency response

In recent times, the field of artificial intelligence (AI) has been transformed by the introduction of large language models (LLMs). These models, popularized by OpenAI’s GPT-3, have demonstrated the emergent capabilities of AI in comprehending and producing text resembling human language, which has helped them transform several industries. But its role has yet to be explored in the nuclear industry, specifically in managing radiation emergencies. The present work explores LLMs’ contextual awareness, natural language interaction, and their capacity to comprehend diverse queries in a radiation emergency response setting. In this study we identify different user types and their specific LLM use-cases in radiation emergencies. Their possible interactions with ChatGPT, a popular LLM, has also been simulated and preliminary results are presented. Drawing on the insights gained from this exercise and to address concerns of reliability and misinformation, this study advocates for expert guided and domain-specific LLMs trained on radiation safety protocols and historical data. This study aims to guide radiation emergency management practitioners and decision-makers in effectively incorporating LLMs into their decision support framework.

Making Artificial Intelligence Your Friend, Not Your Foe, in the Literacy Classroom

AbstractThe recent development of ChatGPT, an artificial intelligence application that mimics human language, has many educators questioning its place in the classroom. This article provides suggestions for instructional uses of ChatGPT in the literacy classroom so that teachers can empower students to use this tool safely and effectively for learning.

Early humans out of Africa had only base-initial numerals

The vast majority of languages have numerals involving multiplication. Cross-linguistically, a numeral that involves a multiplier and a numeral base can be base-final, e.g., three hundred [three × hundred] in English, or base-initial, e.g., ikie ita [hundred × three] in Ibibio (Niger-Congo). A worldwide survey of 4099 languages reveals that 39% of the languages are base-initial, 48% are base-final, 4% use both orders, and 8% are without numeral bases. As the first step towards explaining this diversity and worldwide distribution, we offer convergent evidence to support the hypothesis that the languages of early humans in Africa had base-initial numerals. From a linguistic point of view, linearization is necessary for the verbal expression of multiplicative numerals. Between the two linear orders of multiplication, we demonstrate that the base-initial order has an initial advantage in communicative efficiency. We also offer typological evidence from the dominant head-initial word order in present-day numeral systems and nominal phrases in African languages. Finally, results from a phylogenetic analysis based on a global tree of human languages show that the base-initial order is more stable diachronically and more likely to be at the root of the reconstructed tree of languages in Africa between 100 and 150 thousand years ago. The dominant base-final order in non-African languages of modernity is thus likely to be a development after the Out-of-Africa exodus between 60 and 80 thousand years ago.

Social Brain Perspectives on the Social and Evolutionary Neuroscience of Human Language.

Human language and social cognition are two key disciplines that have traditionally been studied as separate domains. Nonetheless, an emerging view suggests an alternative perspective. Drawing on the theoretical underpinnings of the social brain hypothesis (thesis of the evolution of brain size and intelligence), the social complexity hypothesis (thesis of the evolution of communication), and empirical research from comparative animal behavior, human social behavior, language acquisition in children, social cognitive neuroscience, and the cognitive neuroscience of language, it is argued that social cognition and language are two significantly interconnected capacities of the human species. Here, evidence in support of this view reviews (1) recent developmental studies on language learning in infants and young children, pointing to the important crucial benefits associated with social stimulation for youngsters, including the quality and quantity of incoming linguistic information, dyadic infant/child-to-parent non-verbal and verbal interactions, and other important social cues integral for facilitating language learning and social bonding; (2) studies of the adult human brain, suggesting a high degree of specialization for sociolinguistic information processing, memory retrieval, and comprehension, suggesting that the function of these neural areas may connect social cognition with language and social bonding; (3) developmental deficits in language and social cognition, including autism spectrum disorder (ASD), illustrating a unique developmental profile, further linking language, social cognition, and social bonding; and (4) neural biomarkers that may help to identify early developmental disorders of language and social cognition. In effect, the social brain and social complexity hypotheses may jointly help to describe how neurotypical children and adults acquire language, why autistic children and adults exhibit simultaneous deficits in language and social cognition, and why nonhuman primates and other organisms with significant computational capacities cannot learn language. But perhaps most critically, the following article argues that this and related research will allow scientists to generate a holistic profile and deeper understanding of the healthy adult social brain while developing more innovative and effective diagnoses, prognoses, and treatments for maladies and deficits also associated with the social brain.

Text emotional analysis in Natural Language Processing

Natural Language Processing (NLP) is an important research direction in the field of artificial intelligence, aimed at enabling computers to understand and process human natural language. Emotional analysis refers to the use of computer technology to automatically recognize and classify the emotional tendencies expressed in text, such as positive, negative, or neutral. This technology can be applied to fields such as social media analysis, public opinion monitoring, market research, etc. The goal is to enable computers to understand and interpret emotional information in text like humans. Text sentiment analysis, as a key task of NLP, aims to identify and classify emotional tendencies expressed in text. This article will delve into the field of text sentiment analysis in natural language processing, focusing on the role and application of standard sentiment dictionaries and corpora in sentiment analysis. Reviewing relevant research conclusions, providing reference and inspiration for achieving more accurate and comprehensive text sentiment analysis through research and exploration.

Embodied human language models vs. Large Language Models, or why Artificial Intelligence cannot explain the modal be able to

Embodied human language models vs. Large Language Models, or why Artificial Intelligence cannot explain the modal be able to

A case for animal reference: beyond functional reference and meaning attribution

Reference is a basic feature of human language. A much debated question in the scholarship on animal communication and language evolution is whether traces of the human capacity for reference can be found in animals too. Do animals refer to things with their signals in the manner that humans do? Or is reference something that is unique to human communication? Answers to these questions have shifted significantly over the years and remain contentious. In this paper, I start by reconstructing and critically analysing three influential discussions of the ways in which animal signals refer: the theory of functional reference (Marler, Evans and Hauser, 1992), Wheeler and Fischer (2012)’s meaning attribution framework, and Scarantino (2013)’s revised definition of functional reference. I show that functional reference, both in its traditional and revised version, as well as the meaning attribution framework, fail to adequately characterise animal reference as an evolutionary precursor of linguistic reference. This is because they all overlook at least some aspects of the psychology of signal production. Nonetheless, drawing on Crockford et al. (2012, 2017), I show that we can plausibly interpret chimpanzees’ alert hoos as a case of intentional, human-like animal reference.

A Literature Survey on Quadruped AI Assistant: Integrating Image Processing and Natural Language Processing for Emotional Intelligence

At the nexus of artificial intelligence, robotics, image processing, and natural language processing (NLP), quadruped AI assistants provide a revolutionary method of interacting with machines. Intending to give quadruped AI assistants emotional intelligence, this literature review methodically looks over and compiles the body of research, concentrating on how to use natural language processing and image processing together. The review seeks to offer a comprehensive grasp of the developments, challenges, and possible uses in this diverse subject. The initial section of the survey provides an overview of quadruped robots and highlights how they are integrated with image processing technology to provide visual perception. It explores the various locomotion techniques, demonstrating how these robots use picture data to improve their capacity to navigate and adapt to different situations. The conversation also touches on sensor technologies, highlighting their function in obtaining and deciphering visual data for intelligent interaction. In addition, the research delves into how quadruped AI assistants include natural language processing and examines how these robots interpret and react to instructions in human language. One of the main topics of debate is how sentiment analysis and emotional recognition methods might help these assistants become more emotionally intelligent. Finally, this review of the literature offers a comprehensive viewpoint on how natural language processing and image processing are integrated in quadruped AI assistants, providing academics and practitioners with an outline for developing emotional intelligence in these robots. Compiling knowledge from robotics, artificial intelligence, image processing, and natural language processing is essential to creating emotionally competent quadruped AI assistants and ensuring their smooth incorporation into human-centered settings.This research also investigates the integration of Natural Language Processing and Image Processing for Emotional Intelligence in Quadruped AI Assistants and thoroughly assesses the gyroscope functioning on these robotic systems

The evolution, applications, and future prospects of large language models: An in-depth overview

The evolution of natural language processing has transpired through three primary phases, with large-scale language models significantly transforming the field. These models have heightened the machine's capability to understand, produce, and interact with human language in unprecedented ways. Progressing from RNNs to transformer models, transitioning from encoder-decoder frameworks to decoder-centric designs, and the journey from BERT to the Chat-GPT series have marked significant shifts in the academic discourse. Impressively, these sophisticated models have infiltrated a range of sectors, including finance, healthcare, biology, and education, revolutionizing both traditional and emerging domains. However, as these advancements are celebrated, the ethical and economic challenges they introduce must also be addressed. Confronting these pivotal issues and harnessing technology for societal betterment has become a priority for academia and industry alike, sparking intense research endeavors in recent times. This review dives into the history of natural language processing, highlighting the pivotal developments and core principles of large language models. It provides a comprehensive perspective on their adoption and influence within the financial sector, crafting a detailed narrative of their deployment. In conclusion, the analysis reflects on the current challenges posed by these models and presents potential solutions. This study stands as a definitive guide, offering readers an in-depth understanding of the development, application, and future trajectories of large-scale language models.

Computational phylogenetics reveal histories of sign languages.

Sign languages are naturally occurring languages. As such, their emergence and spread reflect the histories of their communities. However, limitations in historical recordkeeping and linguistic documentation have hindered the diachronic analysis of sign languages. In this work, we used computational phylogenetic methods to study family structure among 19 sign languages from deaf communities worldwide. We used phonologically coded lexical data from contemporary languages to infer relatedness and suggest that these methods can help study regular form changes in sign languages. The inferred trees are consistent in key respects with known historical information but challenge certain assumed groupings and surpass analyses made available by traditional methods. Moreover, the phylogenetic inferences are not reducible to geographic distribution but do affirm the importance of geopolitical forces in the histories of human languages.

The Growing Impact of Natural Language Processing in Healthcare and Public Health.

Natural Language Processing (NLP) is a subset of Artificial Intelligence, specifically focused on understanding and generating human language. NLP technologies are becoming more prevalent in healthcare and hold potential solutions to current problems. Some examples of existing and future uses include: public sentiment analysis in relation to health policies, electronic health record (EHR) screening, use of speech to text technology for extracting EHR data from point of care, patient communications, accelerated identification of eligible clinical trial candidates through automated searches and access of health data to assist in informed treatment decisions. This narrative review aims to summarize the current uses of NLP in healthcare, highlight successful implementation of computational linguistics-based approaches, and identify gaps, limitations, and emerging trends within the subfield of NLP in public health. The online databases Google Scholar and PubMed were scanned for papers published between 2018 and 2023. Keywords "Natural Language Processing, Health Policy, Large Language Models" were utilized in the initial search. Then, papers were limited to those written in English. Each of the 27 selected papers was subject to careful analysis, and their relevance in relation to NLP and healthcare respectively is utilized in this review. NLP and deep learning technologies scan large datasets, extracting valuable insights in various realms. This is especially significant in healthcare where huge amounts of data exist in the form of unstructured text. Automating labor intensive and tedious tasks with language processing algorithms, using text analytics systems and machine learning to analyze social media data and extracting insights from unstructured data allows for better public sentiment analysis, enhancement of risk prediction models, improved patient communication, and informed treatment decisions. In the recent past, some studies have applied NLP tools to social media posts to evaluate public sentiment regarding COVID-19 vaccine use. Social media data also has the capacity to be harnessed to develop pandemic prediction models based on reported symptoms. Furthermore, NLP has the potential to enhance healthcare delivery across the globe. Advanced language processing techniques such as Speech Recognition (SR) and Natural Language Understanding (NLU) tools can help overcome linguistic barriers and facilitate efficient communication between patients and healthcare providers.