Computational Linguistics Research Articles

Rhetorical analysis techniques in Dante Alighieri's Divine Comedy

The diversity of languages of the digital real has led to the creation of new communication models which incorporate formal, synthetic, and approved-by-areas codes for smart communication. The emblem of our society is the Mood virtual communicator: this replaces words and sentences with algorithms and acronyms. Our course of direction is heading inexorably towards digital codes that transmit pre-established semantics generated by a fixed sentence. Thus, it makes sense to discuss communication styles. And what does education consist of? Although we can take sides with fixed and standardized synthetic communication, we also call for respect for traditional and cultural values in individual societies. Still, we call into question the methodologies of study and reference models, which are currently used for virtual communication. The disciplines of theoretical linguistics, computational linguistics, mathematics, quantum physics, sociology, psychology, pedagogical theories, and other related subjects are all included in Social Sciences and Digital Media. This work aims to present an unwavering logical progression that transitions from normative grammar to formal grammar to then examine the features of a poetic text, such as Dante Alighieri's Divine Comedy, from several perspectives.

The Sociology of Interpretation

Recent years have seen a growing sociological interest in meaning. In fact, some argue that sociology cannot confront its foundational questions without addressing meaning. Yet sociologists mean many things when they talk about meaning. We propose a practical approach that conceptualizes meaning as an instance of an actor interpreting a stimulus. Reviewing existing literature, we find that most sociological accounts understand interpretation either as categorization or as semantic association. We show that an integrated approach is analytically useful for conceptualizing shared interpretation and the process by which people coordinate their interpretations. This provides a framework for addressing interpretative heterogeneity when studying attitudinal or behavioral variance. We conclude by highlighting how recent advances in computational linguistics have opened exciting new possibilities for the study of interpretation, and suggest several avenues for future research.

Computational linguistics at the crossroads: A comprehensive review of NLP advancements

New NLP breakthroughs have put Computational Linguistics at a crossroads. NLP's past, present, and future are covered. This review explains computational linguistics' creation with a brief history of linguistics and computer science. Early solutions processed and understood natural language using rule-based systems using manually constructed linguistic rules. Over time, these tactics became increasingly problematic as language became more complex and obscure. Statistical approaches transformed operations. Neural network-based machine learning methods are leading the area because they can learn complicated patterns and representations from large text collections. A data-driven model revolution in natural language processing enhanced language modelling, machine translation, and sentiment analysis. Next, NLP improvements for several tasks and applications are evaluated. Language understanding models that capture semantic nuances and contextual relationships use deep learning frameworks. Word embeddings and transformer-based architectures like GPT and BERT perform well on benchmark datasets for text classification, question answering, and named item identification. The paper also shows how NLP interacts with computer vision, voice processing, and other domains to show the merits and cons of cross-disciplinary research. Multimodal techniques that combine text, graphics, and audio may increase natural language processing and interpretation. The review discusses NLP's effects on prejudice, justice, and privacy. Responsible development and implementation are needed when NLP technology becomes widespread due to algorithmic bias and data privacy concerns. NLP research directions and concerns are reviewed. Existing models may meet standards but fail in practice.

Identifying somatic fingerprints of cancers defined by germline and environmental risk factors.

Numerous studies over the past generation have identified germline variants that increase specific cancer risks. Simultaneously, a revolution in sequencing technology has permitted high-throughput annotations of somatic genomes characterizing individual tumors. However, examining the relationship between germline variants and somatic alteration patterns is hugely challenged by the large numbers of variants in a typical tumor, the rarity of most individual variants, and the heterogeneity of tumor somatic fingerprints. In this article, we propose statistical methodology that frames the investigation of germline-somatic relationships in an interpretable manner. The method uses meta-features embodying biological contexts of individual somatic alterations to implicitly group rare mutations. Our team has used this technique previously through a multilevel regression model to diagnose with high accuracy tumor site of origin. Herein, we further leverage topic models from computational linguistics to achieve interpretable lower-dimensional embeddings of the meta-features. We demonstrate how the method can identify distinctive somatic profiles linked to specific germline variants or environmental risk factors. We illustrate the method using The Cancer Genome Atlas whole-exome sequencing data to characterize somatic tumor fingerprints in breast cancer patients with germline BRCA1/2 mutations and in head and neck cancer patients exposed to human papillomavirus.

Ainu–Japanese Bi-directional Neural Machine Translation: A Step Towards Linguistic Preservation of Ainu, An Under-Resourced Indigenous Language in Japan

This study presents a groundbreaking approach to preserving the Ainu language, recognized as critically endangered by UNESCO, by developing a bi-directional neural machine translation (MT) system between Ainu and Japanese. Utilizing the Marian MT framework, known for its effectiveness with resource-scarce languages, the research aims to overcome the linguistic complexities inherent in Ainu's polysynthetic structure. The paper delineates a comprehensive methodology encompassing data collection from diverse Ainu text sources, meticulous preprocessing, and the deployment of neural MT models, culminating in the achievement of significant SacreBLEU scores that underscore the models' translation accuracy. The findings illustrate the potential of advanced MT technology to facilitate linguistic preservation and educational endeavors, advocating for integrating such technologies in safeguarding endangered languages. This research not only underscores the critical role of MT in bridging language divides but also sets a precedent for employing computational linguistics to preserve cultural and linguistic heritage.

Veritas AI: The ChatGPT Polygraph

Aims: The objective of Veritas AI is to revolutionize the domain of lie detection through the deployment of a cutting-edge algorithm within the realms of computational linguistics and artificial intelligence. Study Design: Veritas AI is conceptualized as a groundbreaking framework that integrates advanced syntactic and semantic analysis, leveraging generative pre-trained transformers to identify linguistic cues indicative of deception. Place and Duration of Study: The research underpinning Veritas AI’s algorithm was meticulously executed at the Abacus CSE Lab over a period from December 2022 to March 2024, ensuring a robust empirical foundation for the system’s validation and optimization. Methodology: Employing a deep learning neural network at its core, Veritas AI is trained on a diverse dataset comprising both truthful and deceptive dialogues. This training is complemented by multimodal biometric interrogation techniques and sophisticated natural language processing algorithms. Results: The empirical results underscore Veritas AI’s unparalleled accuracy in discerning truth, marked by its ability to provide real-time adaptive feedback and maintain robust performance across various communication scenarios. Conclusion: In conclusion, Veritas AI stands as a testament to the symbiotic potential of human ingenuity and machine learning. Its precision-engineered algorithm, underpinned by empirical validation, heralds a transformative leap in the field of automated veracity assessment, setting a new benchmark for truth analysis in the digital age.

Rethinking Plagiarism in the Era of Generative AI

The emergence of generative artificial intelligence (AI) technologies, such as large language models (LLMs) like ChatGPT, has precipitated a paradigm shift in the realms of academic writing, plagiarism, and intellectual property. This article explores the evolving landscape of English composition courses, traditionally designed to develop critical thinking through writing. As AI becomes increasingly integrated into the academic sphere, it necessitates a reevaluation of originality in writing, the purpose of learning research and writing, and the frameworks governing intellectual property (IP) and plagiarism. The paper commences with a statistical analysis contrasting the actual use of LLMs in academic dishonesty with educator perceptions. It then examines the repercussions of AI-enabled content proliferation, referencing the limitation of three books self-published per day in September 2023 by Amazon due to a suspected influx of AI-generated material. The discourse extends to the potential of AI in accelerating research akin to the contributions of digital humanities and computational linguistics, highlighting its accessibility to the general public. The article further delves into the implications of AI on pedagogical approaches to research and writing, contemplating its impact on communication and critical thinking skills, while also considering its role in bridging the digital divide and socio-economic disparities. Finally, it proposes revisions to writing curricula, adapting to the transformative influence of AI in academic contexts. 

Mobile/VR/Robotics/IoT-Based Chatbots and Intelligent Personal Assistants for Social Inclusion

Chatbots are dialogue systems that utilize computational linguistics (CL), including automatic speech recognition (ASR) and natural language processing (NLP). Artificially intelligent conversational agents combine personalization, interoperability, and scalability with the aim of promoting safe information monitoring, management, and retrieval. AI chatbots offer user-friendly engagement for innovative e-learning, care assistance, and multilingual digital content creation, promoting inclusiveness and AI-enhanced communication. They are a breakthrough for future societies and economies as they can offer cost-effective, tailor-made, and instant exchange of knowledge and skills, including on-site assistance, health monitoring, and e-consultation. AI chatbots are constantly improving to the benefit of their users. Their economic and social impact is expected to rise as individuals, especially vulnerable populations, are trained to use them for lifelong learning, decision-making, and problem-solving.

Neurocomputer System of Semantic Analysis of the Text in the Kazakh Language

The purpose of the study is to solve an extreme mathematical problem—semantic analysis of natural language, which can be used in various fields, including marketing research, online translators, and search engines. When training the neural network, data training methods based on the latent Dirichlet allocation model and vector representation of words were used. This study presents the development of a neurocomputer system used for the purpose of semantic analysis of the text in the Kazakh language, based on machine learning and the use of the latent Dirichlet allocation model. In the course of the study, the stages of system development were considered, regarding the text recognition algorithm. The Python programming language was used as a tool using libraries that greatly simplify the process of creating neural networks, including the Keras library. An experiment was conducted with the involvement of experts to test the effectiveness of the system, the results of which confirmed the reliability of the data provided by the system. The papers of modern computer linguists dealing with the problems of natural language processing using various technologies and methods are considered.

Semantic minimalism and the continuous nature of polysemy

Polysemy has recently emerged as a popular topic in philosophy of language. While much existing research focuses on the relatedness among senses, this article introduces a novel perspective that emphasizes the continuity of sense individuation, sense regularity, and sense productivity. This new perspective has only recently gained traction, largely due to advancements in computational linguistics. It also poses a serious challenge to semantic minimalism, so I present three arguments against minimalism from the continuous perspective that touch on the minimal concept, the distinction from homonymy, and the quasi‐rule‐like nature of polysemy. Last, I provide an account of polysemy that incorporates this continuous perspective.

The language network as a natural kind within the broader landscape of the human brain.

Language behaviour is complex, but neuroscientific evidence disentangles it into distinct components supported by dedicated brain areas or networks. In this Review, we describe the 'core' language network, which includes left-hemisphere frontal and temporal areas, and show that it is strongly interconnected, independent ofinput and output modalities, causally important for language and language-selective. We discuss evidence that this language network plausibly stores language knowledge and supports core linguistic computations related to accessing words and constructions from memory and combining them to interpret (decode) or generate (encode) linguistic messages. We emphasize that the language network works closely with, but is distinct from, both lower-level - perceptual and motor - mechanisms and higher-level systems of knowledge and reasoning. The perceptual and motor mechanisms process linguistic signals, but, in contrast to the language network, are sensitive onlyto these signals' surface properties, not their meanings; the systems of knowledge and reasoning (such as the system that supports social reasoning) are sometimes engaged during language use but are not language-selective. This Review lays a foundation both for in-depth investigations of these different components of the language processing pipeline and for probing inter-component interactions.

IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN ENERGY CONSUMPTION CALCULATIONS TO REDUCE EXCESS GENERATION IN THE CONTEXT OF UKRAINE'S RECOVERY

In authors' opinion, the relevance of implementing artificial intelligence in the calculation of energy consumption in order to reduce excess generation lies in several key aspects: 1) efficient use of resources (by analysing data and predicting energy consumption patterns using artificial intelligence, the operation of the energy system can be optimised, ensuring efficient use of energy resources and avoiding excessive electricity generation); 2) reduction of losses (artificial intelligence can help identify and eliminate problematic segments in energy systems, leading to a reduction in energy losses during transport and distribution); 3) consumption forecasting (artificial intelligence can predict and respond to energy consumption peaks, ensuring the stability of energy supply and avoiding overloading of energy systems); 4) resource conservation and emissions reduction (efficient management of energy consumption using artificial intelligence can lead to reduced fuel consumption and greenhouse gas emissions, promoting more sustainable and environmentally friendly development). Artificial intelligence is a field of computer science that deals with the creation of programs and systems capable of performing tasks that typically require human intellectual abilities. These systems can exhibit cognitive functions such as image recognition, language understanding, decision making, self-learning and planning. Artificial intelligence uses methods and techniques from computer science, mathematics, linguistics, philosophy and other fields to design and implement intelligent systems. Artificial intelligence in the energy sector is the application of AI methods and technologies to optimise energy production, transmission, distribution and consumption. This includes the development of algorithms and systems that can automatically analyse large amounts of data, predict energy demand, optimise energy processes, maintain the stability of energy networks and reduce energy losses. The application of artificial intelligence to energy can help increase the efficiency of energy production, reduce environmental impact and improve the reliability of energy systems. The subject of the study is the introduction of artificial intelligence in the calculation of energy consumption in order to reduce excess generation in the context of Ukraine's recovery. The research methods for introducing artificial intelligence into the calculation of energy consumption in order to reduce excess generation in the context of Ukraine's recovery are a system of general scientific and special methods of scientific knowledge. The purpose of the study is to determine the possibilities of introducing artificial intelligence into the calculation of energy consumption to reduce excess generation in the context of Ukraine's recovery. Results. Investing in the use of artificial intelligence to calculate energy consumption in order to reduce excess generation in the context of Ukraine's recovery can be done through various investment instruments, including: venture capital (investments in start-ups and companies developing artificial intelligence technologies to optimise energy consumption); project financing (financing of specific projects using artificial intelligence to analyse and optimise energy consumption); corporate investments (investments in the development of in-house artificial intelligence systems for energy efficiency management at industrial enterprises); stock market (investments in shares and bonds of companies specialising in the development and implementation of innovative technologies for the energy sector); crowdfunding (raising funds from individual investors on platforms dedicated to the development of artificial intelligence projects in the field of energy efficiency).

A computational linguistic methodology for assessing semiotic structure in prehistoric art and the meaning of southern Scandinavian Mesolithic ornamentation

Non-figurative prehistoric art is comparatively common yet challenging to decode and hence to understand. At the heart of the difficulty of assessing the presence of semiotic structure in prehistoric art is a lack of appropriate, replicable, and case-transferable methodologies. We here propose a novel approach derived from computational linguistics, in which k-skip-n-gram (skipgram) models and associated pointwise mutual information (PMI) measures are customised to the analysis of prehistoric art. In applying this methodology to a large corpus of portable art from the South Scandinavian Mesolithic, we demonstrate how the mutual relationship between individual motifs can be established. In the case of Mesolithic portable art, our analysis strongly suggests that there is no evident semiotic structure – this was likely not a form of proto-writing – but salient changes of motif occurrence over time are detectable. These changes are sensitive to changes in population density, structure, and connectedness, and may relate to increased territoriality in the Late Mesolithic. The method presented here is readily case-transferable and renders possible further linguistic and semiotic analyses of prehistoric art.

MODELING ANALYTICAL VERB FORMS FOR THE MORPHOLOGICAL ANALYZER OF THE UZBEK LANGUAGE

This paper explores the complexities involved in handling lexemes containing multiple word forms within the field of computational linguistics. It investigates diverse approaches and technological advancements, encompassing both syntactic and semantic analyses. Furthermore, it addresses the challenges encountered in Uzbek linguistics regarding the translation of analytical forms consisting of multiple word forms, along with potential solutions. Additionally, it emphasizes the importance of examining lexemes with multiple word forms within computational linguistics, offering insights into pioneering researchers in this field. The intricacies inherent in processing such lexemes in computational linguistics arise from their ambiguity, varied forms, and meanings, necessitating context consideration for accurate parsing of word combinations. The article also advocates for the development of effective analytical methods capable of capturing the intended meaning of expressions.

Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach.

Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic "hidden genome" model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types. Consistent, scalable point estimation of the model can incorporate 10s of millions of variants across thousands of tumors and permit impressive prediction and attribution. However, principled statistical inference is infeasible due to the volume, correlation, and noninterpretability of mutation contexts. In this paper, we propose a novel framework that leverages topic models from computational linguistics to effectuate dimension reduction of mutation contexts producing interpretable, decorrelated meta-feature topics. We propose an efficient MCMC algorithm for implementation that permits rigorous full Bayesian inference at a scale that is orders of magnitude beyond the capability of existing out-of-the-box inferential high-dimensional multi-class regression methods and software. Applying our model to the Pan Cancer Analysis of Whole Genomes dataset reveals interesting biological insights including somatic mutational topics associated with UV exposure in skin cancer, aging in colorectal cancer, and strong influence of epigenome organization in liver cancer. Under cross-validation, our model demonstrates highly competitive predictive performance against blackbox methods of random forest and deep learning.

Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis

Natural Language Processing (NLP) is an interdisciplinary field of computer science, artificial intelligence, and linguistics that focuses on the ability of computers to understand, process, generate, and simulate human language in order to achieve the ability to have natural conversations with humans. The underlying principles of natural language processing are at multiple levels, including linguistics, computer science, and statistics. It involves the study of language structure, semantics, grammar and pragmatics, as well as the statistical analysis and modeling of large-scale corpora. In the process of concrete implementation, it is necessary to process natural language at multiple levels. Based on this, this paper combined deep learning and natural language processing technology to conduct sentiment analysis on patients' comments, so as to recommend drugs that are more suitable for patients, thus achieving accurate drug prescribing and personalized recommendation.

ABSA for Chinese review with fine-tuned BERT and zero-shot classification

In recent years, there has been a trend towards utilizing Large Language Models (LLMs) to address diverse issues in computational linguistics. More parameters and layers make them better suited to approximating the objective function in multi-domain tasks. In the twenty-first century, since e-commerce has grown quickly, people's interest in gleaning insightful information from other people's evaluations has grown. Conducting fine-grained sentiment analysis is becoming more beneficial. Aspect-Based Sentiment Analysis (ABSA) is a sub-problem within the field of Sentiment Analysis. The ABSA problem encompasses two sub-problems: Identification of targets and Opinion Mining. Existing methods address ABSA well with a relationship between the aspect and the labels. It is the most common situation in application scenarios. This paper innovatively proposes an ABSA method based on zero-shot classification combined and fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. The method is used experimentally in five tags (1-5) at the "service" and "environment" levels. In the experiment, the predictions of the method-derived model are contrasted with those of the original model. The results show a substantial improvement in the model's prediction accuracy in different aspects.

Emotion Detection from Speech: A Comprehensive Approach Using Speech-to-Text Transcription and Ensemble Learning

The field of text-to-emotion analysis is investigated in this study, which uses an interactive methodology to reveal subtle emotional insights in textual data. The research explores the complex relationship between language and emotion using sophisticated methods without focusing on any particular frontend or backend technology. The research attempts to improve our understanding of how literary information transmits emotional subtleties by emphasizing a broad but methodical examination. The lack of mentions of particular libraries and backends indicates an emphasis on the general ideas and techniques used in text-to-emotion analysis. The findings demonstrate the possibility of deriving significant emotional context from text, opening doors for applications in a variety of fields where user sentiment analysis is essential. This study adds to the body of knowledge on emotional intelligence in computational linguistics and lays the groundwork for future developments in text analysis techniques.

Enhancing Natural Language Processing (NLP) through VIKOR Method: A Comprehensive Approach for Improved Computational Linguistics

Natural Language Processing (NLP) is a revolutionary discipline that resides at the crossroads of computer science and linguistics, with its primary emphasis on the interaction between computers and human language. With roots in artificial intelligence, NLP seeks to equip machines with the ability to comprehend, interpret, and respond to natural language, enabling more intuitive and meaningful communication between humans and computers. This multidisciplinary domain leverages advanced algorithms and models to tackle a range of linguistic challenges, from language translation and sentiment analysis to speech recognition. Recent breakthroughs, particularly in deep learning and neural networks, have propelled NLP to new heights, with applications spanning diverse sectors such as healthcare, finance, and education. As NLP continues to advance, its potential impact on enhancing human-machine interaction and information processing is increasingly evident, promising a future where technology seamlessly integrates with our natural modes of communication. The significance of Natural Language Processing (NLP) in research lies in its capacity to revolutionize human-computer interaction. NLP empowers machines to understand and generate human language, enabling advancements in sentiment analysis, language translation, and conversational AI. This transformative capability has profound implications across various industries, including healthcare, finance, and education. NLP research not only enhances the efficiency of information retrieval and processing but also holds the key to developing more intuitive and user-friendly technologies. As the frontier of NLP research expands, its potential to bridge the communication gap between humans and machines continues to shape the future of technology and information access. VIKOR functions as a multi-criteria decision-making technique that determines the best option by comparing it to the ideal option. The ranking procedure comprises determining distances concerning the optimal solution. VIKOR uses linear normalization to get the best possible outcomes. This technique was first presented by Opricovic in 1998 and was intended to optimize multi-attribute complex systems. Its main focus is on ranking lists that allow for flexibility in the strategy weight interval and incorporate compromise alternatives to obtain desired results. Alternative taken as Open AI GPT-4, Google BERT, Microsoft Azure Text Analytics, IBM Watson Natural Language Understanding, spaCy, NLTK (Natural Language Toolkit), Amazon Comprehend. Evaluation Parameter taken as Sentiment Analysis (F1 Score), NER Performance, Language Support, Processing Speed (Response Time) (ms), Translation Accuracy (BLEU Score), Data Privacy. The result it is seen that Amazon Comprehend is got the first rank where as is the IBM Watson Natural Language Understanding is having the lowest rank.

Making Sense of Citizens’ Input through Artificial Intelligence: A Review of Methods for Computational Text Analysis to Support the Evaluation of Contributions in Public Participation

Public sector institutions that consult citizens to inform decision-making face the challenge of evaluating the contributions made by citizens. This evaluation has important democratic implications but at the same time, consumes substantial human resources. However, until now the use of artificial intelligence such as computer-supported text analysis has remained an under-studied solution to this problem. We identify three generic tasks in the evaluation process that could benefit from natural language processing (NLP). Based on a systematic literature search in two databases on computational linguistics and digital government, we provide a detailed review of existing methods and their performance. While some promising approaches exist, for instance to group data thematically and to detect arguments and opinions, we show that there remain important challenges before these could offer any reliable support in practice. These include the quality of results, the applicability to non-English language corpuses and making algorithmic models available to practitioners through software. We discuss a number of avenues that future research should pursue that can ultimately lead to solutions for practice. The most promising of these bring in the expertise of human evaluators, for example through active learning approaches or interactive topic modeling.