Ambiguous Language Research Articles

The Impact of Sticky Note Strategy on Students' Vocabulary

This research entitled (The jokes of ''Rishtai Mirwari'' from the angle of linguistics ambiguity), is an attempt to analyze its jokes in terms of meaning. Ambiguity is generally viewed as an aspect of the semantic level, so it is assumed that ambiguity plays a major role in the production and creation of humor; Especially the jokes of the "Rishtai Mirwari", so the study aims to demonstrate how ambiguity affects the construction of jokes. It also wants to answer an issue, such as; the question of whether the phenomenon of ambiguity in language is of particular importance; to what extent is it reflected in the language of humour. Therefore, the study assumed that cultural ambiguity (synonymy, polysemy) are more widely reflected than other types of ambiguity in jokes (Rishtai Mirwari), because this type of ambiguity is mainly at the level of meaning and the ambiguity is reflected as a semantic game in old jokes. The scope of the research, which is within the framework of the volumes of (Rishtai Mirwari), written in Central Kurdish, mainly relies on the method of (analytical-descriptive) statistics. Also, because much has been said about ambiguity in scientific research mediums, ambiguity has been discussed according to the needs of the subject. is an attempt to analyze its jokes in terms of meaning. Ambiguity is generally viewed as an aspect of the semantic level, so it is assumed that ambiguity plays a major role in the production and creation of humor; Especially the jokes of the "Rishtey Mrwari", so the study aims to demonstrate how ambiguity affects the construction of jokes. It also wants to answer an issue, such as; the question of whether the phenomenon of ambiguity in language is of particular importance; to what extent is it reflected in the language of humour. Therefore, the study assumed that cultural ambiguity (Homonymy) are more widely reflected than other types of ambiguity in jokes (Rishtey Mrwari), because this type of ambiguity is mainly at the level of meaning and the ambiguity is reflected as a semantic game in old jokes. The scope of the research, which is within the framework of the volumes of (Rishtey Mrwari), written in Central Kurdish, mainly relies on the method of (analytical-descriptive) statistics. Also, because much has been said about ambiguity in scientific research mediums, ambiguity has been discussed according to the needs of the subject.

The Advancement оf Modern Chatbots аnd Their Applications

Artificial Intelligence (AI) is on the rise, with applications which use it developing very rapidly in recent years. This is very much characteristic of one of its subclasses, Generative Artificial Intelligence, which underpins many of today’s most popular AI applications. Challenges in developing Large language models (LLMs) are discussed, which include difficulties such as language ambiguity, common sense, and understanding complex language constructs and queries. The key innovations that led to the development of LLMs are described, namely the creation of transformers, self-supervised learning and large datasets. Next, the most popular applications of LLM today, chatbots, are discussed. The capabilities, advantages, and disadvantages of some of the most popular chatbots, such as ChatGPT, Copilot, Bard, YouChat, HuggingChat, and Jasper, are shown, and an example of wrong answers from them is given. The advantages of chatbots – answering questions asked in natural language, generating creative content, solving problems – are summarized, and a number of their applications in business and economic fields are shown. Disadvantages are pointed out, including the provision of sometimes inaccurate or misleading information. Finally, expectations for the future development of LLMs and chatbots and the dangers they may bring to people are presented.

L’Entre d’eux: Lacan, Serres and the climate emergency

ABSTRACT My article claims that the acceleration of the generalised planetary ecological crisis has been worsened by a form of collective reasoning called disavowal. To understand why and how this mode of reasoning in the Global North is accelerating climate boiling, I turn to Michel Serres’s Le parasite and Jacques Lacan’s psychoanalysis. I seek to bridge the gap between Serres and Lacan on the question of language and its connection to human intersubjectivity, and show how their conclusions reveal the mechanisms behind the cognitive dissonance at hand in the climate crisis. I claim that we can connect these two philosophers through the concept of the ‘d’eux’, as it relates to intersubjectivity, and I show how they both deploy and rely on the polyvalence and ambiguity of language as a rhetorical strategy to highlight the presence of a ‘third’, which I claim is the Serresian parasite and Lacanian big Other. This theoretical foundation underlines that one of the key aspects of our linguistic reasoning is to simply accept the imperatives of the parasite or the big Other, and thus enables the argument that our collective psychotic reasoning is, through this evil ‘third’, contributing to the generalised planetary ecological crisis, that we disavow. In the words of Jean Oury: ‘hasard’eux’.

A fuzzy description logic based IoT framework: Formal verification and end user programming.

The Internet of Things (IoT) has become one of the most popular technologies in recent years. Advances in computing capabilities, hardware accessibility, and wireless connectivity make possible communication between people, processes, and devices for all kinds of applications and industries. However, the deployment of this technology is confined almost entirely to tech companies, leaving end users with only access to specific functionalities. This paper presents a framework that allows users with no technical knowledge to build their own IoT applications according to their needs. To this end, a framework consisting of two building blocks is presented. A friendly interface block lets users tell the system what to do using simple operating rules such as "if the temperature is cold, turn on the heater." On the other hand, a fuzzy logic reasoner block built by experts translates the ambiguity of human language to specific actions to the actuators, such as "call the police." The proposed system can also detect and inform the user if the inserted rules have inconsistencies in real time. Moreover, a formal model is introduced, based on fuzzy description logic, for the consistency of IoT systems. Finally, this paper presents various experiments using a fuzzy logic reasoner to show the viability of the proposed framework using a smart-home IoT security system as an example.

Application of the transformer model algorithm in chinese word sense disambiguation: a case study in chinese language

This study aims to explore the research methodology of applying the Transformer model algorithm to Chinese word sense disambiguation, seeking to resolve word sense ambiguity in the Chinese language. The study introduces deep learning and designs a Chinese word sense disambiguation model based on the fusion of the Transformer with the Bi-directional Long Short-Term Memory (BiLSTM) algorithm. By utilizing the self-attention mechanism of Transformer and the sequence modeling capability of BiLSTM, this model efficiently captures semantic information and context relationships in Chinese sentences, leading to accurate word sense disambiguation. The model’s evaluation is conducted using the PKU Paraphrase Bank, a Chinese text paraphrase dataset. The results demonstrate that the model achieves a precision rate of 83.71% in Chinese word sense disambiguation, significantly outperforming the Long Short-Term Memory algorithm. Additionally, the root mean squared error of this algorithm is less than 17, with a loss function value remaining around 0.14. Thus, this study validates that the constructed Transformer-fused BiLSTM-based Chinese word sense disambiguation model algorithm exhibits both high accuracy and robustness in identifying word senses in the Chinese language. The findings of this study provide valuable insights for advancing the intelligent development of word senses in Chinese language applications.

Artificial Intelligence Methods in Natural Language Processing: A Comprehensive Review

The rapid evolution of Artificial Intelligence (AI) since its inception in the mid-20th century has significantly influenced the field of Natural Language Processing (NLP), transforming it from a rule-based system to a dynamic and adaptive model capable of understanding the complexities of human language. This paper aims to offer a comprehensive review of the various applications and methodologies of AI in NLP, serving as a detailed guide for future research and practical applications. In the early sections, the paper elucidates the indispensable role of AI in NLP, highlighting its transition from symbolic reasoning to a focus on machine learning and deep learning, and its extensive applications in sectors such as healthcare, transportation, and finance. It emphasizes the symbiotic relationship between AI and NLP, facilitated by platforms like AllenNLP, which aid in the development of advanced language understanding models. Further, the paper explores specific AI techniques employed in NLP, including machine learning, Naive Bayes, and Support Vector Machines, and identifies pressing challenges and avenues for future research. It delves into the applications of AI in NLP, showcasing its transformative potential in tasks such as machine translation, facilitated by deep learning methods, and the development of chatbots and virtual assistants that have revolutionized human-technology interaction. The paper also highlights other fields impacted by AI techniques, including text summarization, sentiment analysis, and named entity recognition, emphasizing the efficiency and accuracy brought about by the integration of AI in these areas. In conclusion, the paper summarizes the remarkable advancements and persistent challenges in NLP, such as language ambiguity and contextual understanding, and underscores the need for diverse and representative labeled data for training. Looking forward, it identifies promising research avenues including Explainable AI, Few-shot and Zero-shot Learning, and the integration of NLP with other data modalities, aiming for a holistic understanding of multimodal data. The paper calls for enhanced robustness and security in NLP systems, especially in sensitive applications like content moderation and fake news detection, to foster trust and reliability in AI technologies. It advocates for continual learning in NLP models to adapt over time without losing previously acquired knowledge, paving the way for a future where AI and NLP work synergistically to understand and generate human language more effectively and efficiently.

Phrase Detection's Impact on Sentiment Analysis of Public Opinion and online Media Toward Political Figures

Public opinion of political figures and policy significantly impacts general elections. Sentiment analysis, as a method to comprehend opinion and emotion in texts, requires the step of text preprocessing to improve data quality. However, textual data often encounters irrelevant words and ambiguous language. These conditions can impact the accuracy of sentiment analysis. Given the significance of precisely interpreting public opinion toward political figures, these issues may result in biased or inaccurate sentiment analysis outcomes. Irregular punctuation or unclear language can disturb the text's intended context, compromising sentiment analysis quality. Additionally, irrelevant words can obscure the focus of the analysis, causing fundamental changes in the original text's meaning. This research focuses on the impact of a specific preprocessing technique, namely Phrase Detection with N-Gram, on sentiment analysis of political figures. By applying this method, the study aims to detail the effects of using Bigram, Trigram, and Unigram on the quality of sentiment analysis, particularly in the context of political figures on Twitter and online media articles. This research indicates that using Bigram in Phrase Detection provides more significant results than Trigram and Unigram for most political figures at Twitter, with the highest accuracy score of 88,23%. Sentiment analysis of articles in online media also indicates various results depending on the type of N-Gram. The results indicate that using N-gram phrase detection can influence the accuracy of sentiment analysis, and the resulting accuracy values are pretty high.

Reforming Digital Trade Rules at the WTO: Clarifying E-Commerce Regulation Policy Space for Economic Inclusion

Purpose: The purpose is to critically analyze the challenges and opportunities associated with reforming digital trade rules at the WTO, in order to explore the implications of ambiguous language and propose new principles and interpretative guidance to refine the rules.
 Methodology: The methodology involves analysis of ambiguous language in existing WTO agreements and proposed e-commerce rules, examination of influence on domestic digital sector capacity, case studies of digital industrial policies and technology regulations, and a proposal for new principles and interpretative guidance.
 Findings: The key findings are that ambiguity allows powerful tech giants to contest policies, impacts nurturing of domestic digital sector capacity, and burdens emerging markets. Case studies illustrate tensions around policy space for inclusive innovation.
 Unique contribution to theory, policy and practice: The unique contributions are the proposal of new principles to refine digital trade rules, development of interpretative guidance to affirm regulatory autonomy rights, and comparison with past WTO pharmaceutical patent waivers debate to emphasize need for adaptive and inclusive regulatory frameworks. Recommendations focus on promoting economic inclusion, innovation, competitiveness, and collaborative approaches in the digital era.

Detection of Questions from Text Data Using LSTM-Deep Learning Model

This paper discusses the importance of detecting questions in textual data for various applications in natural language processing (NLP), such as question answering and chatbot creation. The proposed approach employs long short-term memory (LSTM) models to accurately identify questions by leveraging the sequential nature of language.The paper highlights that LSTM models address challenges like ambiguous language and varying sentence structures. They allow the model to learn from sequential patterns, crucial for understanding the intent behind the text. The preprocessing steps, including tokenization, embedding, and padding, are detailed to prepare the data for training and testing. The study investigates the impact of hyperparameters like hidden layers, hidden states, and optimizer choice on the LSTM algorithm’s performance. In experiments on benchmark datasets, the proposed LSTM-based approach consistently outperforms conventional machine learning models, achieving a remarkable accuracy of 99.25% on the test dataset. The paper concludes by suggesting future directions, including applyingthe approach to other NLP tasks like named entity recognition, sentiment analysis, and text classification. Further optimization for specific datasets or domains is also encouraged. Overall, this research contributes to robust question detection models in NLP, with potential applications in various fields.

On Ambiguity in the Arabic Language: Scrutinizing Translation Issues through Machine Translation from English and French into Arabic

Ambiguity in some syntactic structures of the same language has always posed problems to the human translator and to machine translation. These problems become more complex for the Machine Translation of genetically unrelated languages such as Arabic, English and French. Arabic Lexical ambiguity in Natural Language Processing (NLP) also poses problems when the semantic fields of Arabic words differ from those of English for instance. This often occurs when two or more words from Arabic equate to a single word in English. Semantic gaps between the two languages are also a source of ambiguity in Natural Language Processing. We shall deal with some cases of ambiguity in machine translation from Arabic to English and French and vice versa. The questions addressed in this paper relate to segmentation, determination / non-determination, coordination and the issue of the word as a meaningful and functional unit. Some aspects of the segmentation of constituents into grammatical categories and their comparison with structures of Arabic English and French are addressed in this paper.

Indian Sign Language Recognition Using CNN Inception V3 - A Review

Abstract: This research paper explores the intersection of sign language recognition & deep learning, with a focus on the utilization of CNNs and the Inception V3 architecture. The study emphasizes how crucial proper translation and identification of sign language is to closing the communication gap that exists between the hearing and the Deaf populations. It discusses challenges such as limited datasets and ambiguity in sign language and outlines the potential for future advancements in accessibility and education. By combining the power of deep learning and culturally tailored datasets, this research paper paves the way for more effective sign language recognition, offering the promise of improved communication and accessibility for individuals with hearing impairments. It emphasizes how important it is to remove obstacles to communication and promote inclusion in all facets of daily life for the community of people who are Deaf and hard of hearing

The Aarhus Convention and the Latent Right to a Healthy Environment

Abstract The Aarhus Convention is an ambitious environmental agreement that recognises the right to a healthy environment in its opening article. However, the ambiguous language surrounding this right gives the impression that it is ornamentation and not something requiring serious legal attention. At the same time, the procedural environmental rights which are the centrepiece of the Convention are being eroded, either by design on the part of Contracting Parties or because of a broader malaise about the value of public participation in environmental decision-making. This contribution suggests that closer attention to the legal significance of the substantive right contained in the Convention can help to reinvigorate the procedural Aarhus rights. In so doing, it draws attention to the radical vision of the Aarhus Convention to protect the right of present and future generations to live in an environment adequate to human health and well-being.

Translation Strategies of Ambiguity in English Language and Literature

The translation of English language and literary works has always been crucial for cross-cultural communication. However, a key challenge in translating such works is the accurate communication of ambiguity, which refers to expressions deliberately used in English texts with unclear meanings. These expressions are often poetic and carry deep symbols and implications, adding a unique charm to literary works. This article explores the manifestations of ambiguity in English language and literature and the translation strategies that can be employed to optimize the translation of ambiguity in the English language and literature.

Algorithms Don’t Have A Future: On the Relation of Judgement and Calculation

This paper is about the opposite of judgement and calculation. This opposition has been a traditional anchor of critiques concerned with the rise of AI decision making over human judgement. Contrary to these approaches, it is argued that human judgement is not and cannot be replaced by calculation, but that it is human judgement that contextualises computational structures and gives them meaning and purpose. The article focuses on the epistemic structure of algorithms and artificial neural networks to find that they always depend on human judgement to be related to real life objects or purposes. By introducing the philosophical concept of judgement, it becomes clear that the property of judgement to provide meaning and purposiveness is based on the temporality of human life and the ambiguity of language, which quantitative processes lack. A juxtaposition shows that calculations and clustering can be used and referred to in more or less prejudiced and reflecting as well as opaque and transparent ways, but thereby always depend on human judgement. The paper clearly asserts that the transparency of AI is necessary for their autonomous use. This transparency requires the explicitness of the judgements that constitute these computational structures, thereby creating an awareness of the conditionality of such epistemic entities.

Sensing complicated meanings from unstructured data: a novel hybrid approach

The majority of data on computers nowadays is in the form of unstructured data and unstructured text. The inherent ambiguity of natural language makes it incredibly difficult but also highly profitable to find hidden information or comprehend complex semantics in unstructured text. In this paper, we present the combination of natural language processing (NLP) and convolution neural network (CNN) hybrid architecture called automated analysis of unstructured text using machine learning (AAUT-ML) for the detection of complex semantics from unstructured data that enables different users to make understand formal semantic knowledge to be extracted from an unstructured text corpus. The AAUT-ML has been evaluated using three datasets data mining (DM), operating system (OS), and data base (DB), and compared with the existing models, i.e., YAKE, term frequency-inverse document frequency (TF-IDF) and text-R. The results show better outcomes in terms of precision, recall, and macro-averaged F1-score. This work presents a novel method for identifying complex semantics using unstructured data.

Hybrid Navigation Acceptability and Safety

Autonomous vessels have emerged as a prominent and accepted solution, particularly in the naval defence sector. However, achieving full autonomy for marine vessels demands the development of robust and reliable control and guidance systems that can handle various encounters with manned and unmanned vessels while operating effectively under diverse weather and sea conditions. A significant challenge in this pursuit is ensuring the autonomous vessels' compliance with the International Regulations for Preventing Collisions at Sea (COLREGs). These regulations present a formidable hurdle for the human-level understanding by an autonomous systems as they were originally designed from common navigation practices created since the mid-19th century. Their ambiguous language assumes experienced sailors' interpretation and execution and, therefore, demands a high-level (cognitive) understanding of language and agent intentions that are beyond the capabilities of current state-of-the-art of intelligent system. This position paper highlights the critical requirement of a trustworthy control and guidance system and explores the complexities of adapting COLREGs for safe vessel-on-vessel encounters considering autonomous maritime technology competing and/or cooperating with manned vessels.

Assembling a population health management maturity index using a Delphi method

BackgroundAlthough local initiatives commonly express a wish to improve population health and wellbeing using a population health management (PHM) approach, implementation is challenging and existing tools have either a narrow focus or lack transparency. This has created demand for practice-oriented guidance concerning the introduction and requirements of PHM.MethodsExisting knowledge from scientific literature was combined with expert opinion obtained using an adjusted RAND UCLA appropriateness method, which consisted of six Dutch panels in three Delphi rounds, followed by two rounds of validation by an international panel.ResultsThe Dutch panels identified 36 items relevant to PHM, in addition to the 97 items across six elements of PHM derived from scientific literature. Of these 133 items, 101 were considered important and 32 ambiguous. The international panel awarded similar scores for 128 of 133 items, with only 5 items remaining unvalidated. Combining literature and expert opinion gave extra weight and validity to the items.DiscussionIn developing a maturity index to help assess the use and progress of PHM in health regions, input from experts counterbalanced a previous skewedness of item distribution across the PHM elements and the Rainbow Model of Integrated Care (RMIC). Participant expertise also improved our understanding of successful PHM implementation, as well as how the six PHM elements are best constituted in a first iteration of a maturity index. Limitations included the number of participants in some panels and ambiguity of language. Further development should focus on item clarity, adoption in practice and item interconnectedness.ConclusionBy employing scientific literature enriched with expert opinion, this study provides new insight for both science and practice concerning the composition of PHM elements that influence PHM implementation. This will help guide practices in their quest to implement PHM.

Development and Preliminary Validation of an Equine Brief Pain Inventory for Owner Assessment of Chronic Pain Due to Osteoarthritis in Horses.

An owner-completed questionnaire was designed to monitor the level of chronic pain and impact on quality of life in horses with osteoarthritis (OA). A standardized approach to develop and validate subjective-state scales for clinical use was followed. Scale items were generated through literature review, focus group meetings, and expert panel evaluation. The draft tool was tested for reading level and language ambiguity and piloted in 25 owners/caregivers of horses with osteoarthritis, with factor analysis performed on responses. The resulting revised questionnaire is currently undergoing validation in a larger sample population of 60 OA and 20 sound control horses. In the pilot group, 21 people (84%) found the questionnaire easy to complete and 22 people (88%) found it useful. It could be completed within 5 min by all participants. Readability scores (Flesch Reading Ease Score, Flesch-Kincaid grade level, SMOG index) indicated an English language reading level comparable to that of 6th to 7th grade in the U.S. system (age 11-12 years). Cronbach's alpha of all items in the tool was 0.957, indicating excellent inter-item correlation. Interim analysis for 23 OA horses from the sample population showed good test-retest reliability and higher scores compared to 5 control horses. Full validation must be completed for the instrument to be used in clinical practice.

Engagement against All Odds? Navigating Member States’ Contestation of EU Policy on Kosovo

ABSTRACT Disagreements between European Union (EU) member states constrain the Union’s capacity to manage conflicts such as Kosovo-Serbia. While Kosovo has long received EU support, five EU member states do not recognise its independence. How does the EU manage to work around member states’ vetoes and mitigate contestation? In contrast to previous scholarship, the analysis of the EU enlargement process and visa liberalisation, the Belgrade-Pristina dialogue and the EULEX mission illustrate how institutional, technical and diplomatic solutions have allowed the provision of support to Kosovo, despite internal disagreements. EU member states have delegated to EU institutions the responsibility of overseeing day-to-day conflict management and integration policies concerning Kosovo and Serbia. EU institutions also use technical and constructively ambiguous language to manage conflicts and navigate the absence of political consensus regarding Kosovo’s statehood. Additionally, the EU has fostered diplomatic collaboration with the United States (US) and with actors from the North Atlantic Treaty Organization (NATO) to navigate through the Kosovo-Serbia conflict.

A Minimal Capabilities-Based Account of Loss and Damage

ABSTRACT The topic of loss and damage has generated contentious debates in international policymaking and climate negotiations. Up until now, political agreements have been possible because of the use of ambiguous language in defining loss and damage. However, with the agreement of creating a specific fund for loss and damage reached in the last COP27, the need to define loss and damage becomes more pressing. This definition will not only determine to whom the funds will flow, but also what kind of measures will be funded. This paper contributes to clarifying these two issues. First, it proposes what should count, minimally, as loss and damage by specifying a minimal account of loss and damage based on the capabilities approach. This minimal account develops and justifies an ex-post perspective on loss and damage that is coherent with the UNFCCC discourse. Moreover, it proposes to differentiate between economic damage, non-economic losses, and non-economic damage. Second, it proposes a variety of reparative measures (material and symbolic) that should be implemented in response to different forms of loss and damage.