Linguistic Hedges Research Articles

Features of Hedging Strategies Performed by the Federal Reserve Chair in Press Conferences

This study examined the pragmatic functions of hedging strategies used by the Federal Reserve System (Fed) Chair during press conferences. The Fed’s significant influence on U.S. monetary policy and global financial markets makes its communication methods crucial yet linguistically underexplored. This study employed a mixed-methods approach to examine the frequency and types of hedges, their situational usage, and pragmatic functions. The findings revealed a notable use of approximators and shields to adapt to the complexities of financial communication, with adaptors and plausibility shields being the most common. The Fed Chair employed hedging strategies to address sensitive economic issues, uncertain policies, and matters affecting financial markets and public perception. These hedging strategies were used to mitigate potential criticism, enhance flexibility, soften the tone, maintain credibility, ensure effective information delivery, and improve politeness. The study contributed to the understanding of linguistic hedging strategies in financial communication and underscored the strategic use of language in economic policy-making and financial stability.

A novel maternal thyroid disease prediction using multi-scale vision transformer architecture with improved linguistic hedges neural-fuzzy classifier.

Early pregnancy thyroid function assessment in mothers is covered. The benefits of using load-specific reference ranges are well-established. We pondered whether the categorization of maternal thyroid function would change if multiple blood samples obtained early in pregnancy were used. Even though binary classification is a common goal of current disease diagnosis techniques, the data sets are small, and the outcomes are not validated. Most current approaches concentrate on model optimization, focusing less on feature engineering. The suggested method can predict increased protein binding, non-thyroid syndrome (NTIS) (simultaneous non-thyroid disease), autoimmune thyroiditis (compensated hypothyroidism), and Hashimoto's thyroiditis (primary hypothyroidism). In this paper, we develop an automatic thyroid nodule classification system using a multi-scale vision transformer and image enhancement. Graph equalization is the chosen technique for image enhancement, and in our experiments, we used neural networks with four-layer network nodes. This work presents an enhanced linguistic coverage neuro-fuzzy classifier with chosen features for thyroid disease feature selection diagnosis. The training procedure is optimized, and a multi-scale vision transformer network is employed. Each hop connection in Dense Net now has trainable weight parameters, altering the architecture. Images of thyroid nodules from 508 patients make up the data set for this article. Sets of 80% training and 20% validation and 70% training and 30% validation are created from the data. Simultaneously, we take into account how the number of training iterations, network structure, activation function of network nodes, and other factors affect the classification outcomes. According to the experimental results, the best number of training iterations is 500, the logistic function is the best activation function, and the ideal network structure is 2500-40-2-1. K-fold validation and performance comparison with previous research validate the suggested methodology's enhanced effectiveness.

Comparing the Use of Linguistic Hedging Devices (LHDs) in English and Bahasa Indonesia: Gender and Pedagogical Perspectives

The use of Linguistics Hedging Devices (LHDs) serves a notable function in our daily conversations and thus is thought-provoking to analyze. Although its occurrence tends to be more prominent in spoken communications, the use of this linguistic feature is still frequently apparent in written conversations. Although they do not add much to the proportional content of an utterance, their occurrence remains unique in terms of variations, frequency, and functions across languages. This study examines the use of LHDs by native speakers of English and Bahasa Indonesia in short spontaneous expressions. The data were obtained through Oral Discourse Completion Tasks (ODCTs) involving 40 participants from the two language speakers (20 in English and 20 in Bahasa Indonesia). English respondents expressed their responses in English, while the Indonesian speakers used Bahasa Indonesia in their responses. Each respondent was asked to respond to an apologetic expression from a close friend using a mini-digital tape recording. All responses were transcribed and analyzed, focusing on the number and types of LHDs used by the respondents in their expressions. The results revealed that the two language communities used various LHDs in their responses, mostly intended to show indirectness and reduce threats toward the hearers’ negative faces. English respondents were likely to use these linguistic features more frequently and varied in types than their Indonesian counterparts. A remarkable trend in the study is that Indonesian speakers tended to be more direct in their expressions than their English counterparts, challenging previously reversed claims. Teaching English as a Foreign Language in Indonesia is recommended to introduce various types and uses of these markers and emphasize how they can be used appropriately in authentic contexts.

Attribute implications in multi-adjoint concept lattices with hedges

The computation of (if-then) rules relating the most representative variables of a given dataset is a relevant goal in different areas of data analysis such as stock market prediction, disease diagnosis and census data analysis, among others. In Formal Concept Analysis (FCA), attribute implications follow the philosophy of the if-then rules establishing relationships between sets of attributes. This paper will focus on the theoretical development of attribute implications through the tools provided by the multi-adjoint concept lattice framework. We will show different notions to compute the validity of attribute implications, analyzing their properties and comparing the proposed alternatives. The use of linguistic hedges, and truth-stressing hedges in particular, is an interesting way of incorporating truth functions into attribute implications. Moreover, they have been considered in the computation of the concepts of a given context. This paper also introduces and studies the main properties of the multi-adjoint concept lattices framework with truth-stressing hedges.

Wind power forecasting model based on linguistic fuzzy rules

The design and operationalization of a wind energy system is mainly based on wind speed and wind direction, theses parameters depend on several geographic, temporal, and climatic factors. Fluctuating factors such as climate cause irregularities in wind energy production. Therefore, wind power forecasting is necessary before using wind power systems. Furthermore, in order to make informed decisions, it is necessary to explain the system's predictions to stakeholders. The explainable artificial intelligence (XAI) provides an interactive interface for intelligent systems to interact with machines, validate their results, and trust their behavior. In this paper, we provide an interpretable system for predicting wind energy using weather data. This system is based on a two-step method for fuzzy rules learning clustering (FRLC). The first step uses subtractive clustering and a linguistic approximation to extract linguistic rules. The second step uses linguistic hedges to refine linguistic rules. FRLC is compared to with artificial neural network (ANN), random forest (RF), k-nearest neighbors (K-NN), and support vector regression (SVR) models. The experimental results show that the accuracy of FRLC is acceptable regarding the comparison models and outperform them in terms of the interpretability. In parallel with prediction, FRLC model provides a set of linguistic fuzzy rules that explain the obtained results to the stakeholders.

Wind power forecasting model based on linguistic fuzzy rules

The design and operationalization of a wind energy system is mainly based on wind speed and wind direction, theses parameters depend on several geographic, temporal, and climatic factors. Fluctuating factors such as climate cause irregularities in wind energy production. Therefore, wind power forecasting is necessary before using wind power systems. Furthermore, in order to make informed decisions, it is necessary to explain the system's predictions to stakeholders. The explainable artificial intelligence (XAI) provides an interactive interface for intelligent systems to interact with machines, validate their results, and trust their behavior. In this paper, we provide an interpretable system for predicting wind energy using weather data. This system is based on a two-step method for fuzzy rules learning clustering (FRLC). The first step uses subtractive clustering and a linguistic approximation to extract linguistic rules. The second step uses linguistic hedges to refine linguistic rules. FRLC is compared to with artificial neural network (ANN), random forest (RF), k-nearest neighbors (K-NN), and support vector regression (SVR) models. The experimental results show that the accuracy of FRLC is acceptable regarding the comparison models and outperform them in terms of the interpretability. In parallel with prediction, FRLC model provides a set of linguistic fuzzy rules that explain the obtained results to the stakeholders.

Advanced Fuzzy Sets and Genetic Algorithm Optimizer for Mammographic Image Enhancement

A well-researched field is the development of Computer Aided Diagnosis (CADx) Systems for the benign-malignant classification of abnormalities detected by mammography. Due to the nature of the breast parenchyma, there are significant uncertainties about the shape and geometry of the abnormalities that may lead to an inaccurate diagnosis. These same uncertainties give mammograms a fuzzy character that is essential to the application of fuzzy processing. Fuzzy set theory considers uncertainty in the form of a membership function, and therefore fuzzy sets can process imperfect data if this imperfection originates from vagueness and ambiguity rather than randomness. Fuzzy contrast enhancement can improve edge detection and, by extension, the quality of related classification features. In this paper, classical (Linguistic hedges and fuzzy enhancement functions), advanced fuzzy sets (Intuitionistic fuzzy set (ΙFS), Pythagorean fuzzy set (PFS), and Fermatean fuzzy sets (FFS)), and a Genetic Algorithm optimizer are proposed to enhance the contrast of mammographic features. The advanced fuzzy sets provide better information on the uncertainty of the membership function. As a result, the intuitionistic method had the best overall performance, but most of the techniques could be used efficiently, depending on the problem that needed to be solved. Linguistic methods could provide a more manageable way of spreading the histogram, revealing more extreme values than the conventional methods. A fusion technique of the enhanced mammography images with Ordered Weighted Average operators (OWA) achieves a good-quality final image.

Multi-attribute decision-making based on novel Fermatean fuzzy similarity measure and entropy measure.

To deal with situations involving uncertainty, Fermatean fuzzy sets are more effective than Pythagorean fuzzy sets, intuitionistic fuzzy sets, and fuzzy sets. Applications for fuzzy similarity measures can be found in a wide range of fields, including clustering analysis, classification issues, medical diagnosis, etc. The computation of the weights of the criteria in a multi-criteria decision-making problem heavily relies on fuzzy entropy measurements. In this paper, we employ t-conorms to suggest various Fermatean fuzzy similarity measures. We have also discussed all of their interesting characteristics. Using the suggested similarity measurements, we have created some new entropy measures for Fermatean fuzzy sets. By using numerical comparison and linguistic hedging, we have established the superiority of the suggested similarity metrics and entropy measures over the existing measures in the Fermatean fuzzy environment. The usefulness of the proposed Fermatean fuzzy similarity measurements is shown by pattern analysis. Last but not least, a novel multi-attribute decision-making approach is described that tackles a significant flaw in the order preference by similarity to the ideal solution, a conventional approach to decision-making, in a Fermatean fuzzy environment.

Some novel q‐rung orthopair fuzzy similarity measures and entropy measures with their applications

AbstractIn this paper, we have introduced some new similarity measures for ‐rung orthopair fuzzy sets and discussed their various features. These similarity measures have been constructed with the help of t‐conorms. The suggested similarity measures are more effective than the available ones in computing the similarity between distinct ‐rung orthopair fuzzy sets. With the help of these suggested similarity measures, we have also developed some novel entropy measures for ‐rung orthopair fuzzy sets. The newly proposed entropy measures are more effective than the existing ones in handling linguistic hedges. The applicability of the suggested similarity measures has been demonstrated in pattern analysis and the findings have been found to be better than due to existing ones. The traditional decision‐making method namely, the technique for order preference by similarity to the ideal solution gives us a compromise solution that is either closest to the positive ideal solution and farthest from the negative ideal solution but not both. So, to overcome this major drawback, we suggest a novel multi‐attribute decision‐making method in the ‐rung orthopair fuzzy environment and apply it to a decision‐making problem.

Linguistic Hedge Based Constrained Representation of Interval Type-2 Fuzzy Sets

Linguistic Hedge Based Constrained Representation of Interval Type-2 Fuzzy Sets

Unsymmetrical basic uncertain information with some decision-making methods

Motivated by a specific decision-making situation, this work proposes the concept and definition of unsymmetrical basic uncertain information which is a further generalization of basic uncertain information and can model uncertainties in some new decision-making situations. We show that unsymmetrical basic uncertain information in some sense can model linguistic hedges such as “at least” and “at most”. Formative weighted arithmetic means and induced aggregations are defined for the proposed concept. Rules-based decision making and semi-copula based integral for this concept with some numerical examples are also presented.

Some t-conorm-based distance measures and knowledge measures for Pythagorean fuzzy sets with their application in decision-making

The Pythagorean fuzzy sets are more robust than fuzzy sets and intuitionistic fuzzy sets in dealing with the problems involving uncertainty. To compare two Pythagorean fuzzy sets, distance measures play a crucial role. In this paper, we have proposed some novel distance measures for Pythagorean fuzzy sets using t-conorms. We have also discussed their various desirable properties. With the help of suggested distance measures, we have introduced some new knowledge measures for Pythagorean fuzzy sets. Through numerical comparison and linguistic hedges, we have established the effectiveness of the suggested distance measures and knowledge measures, respectively, over the existing measures in the Pythagorean fuzzy setting. At last, we have demonstrated the application of the suggested measures in pattern analysis and multi-attribute decision-making.

An ameliorated African vulture optimization algorithm to diagnose the rolling bearing defects

In this work, a novel bearing fault identification scheme making use of deep learning has been proposed. Initially, the raw vibration signal is passed through a time-varying filter based empirical mode decomposition (TVF-EMD) to obtain different modes. Filter parameters of TVF-EMD are optimized by a newly developed optimization algorithm i.e. ameliorated African vulture optimization algorithm The Kernel estimate for mutual information has been considered as the fitness index for the developed optimization algorithm. The mode having the least value of fitness index is known as a prominent mode from which sensitive features representing different bearing conditions are extracted. These extracted features help in preparing the data matrix which is further utilised to build fuzzy-based classification models. The results obtained revealed that the linguistic hedge neuro-fuzzy classifier obtained maximum performance with the least computational time. The comparison of the developed method has also been done with other classification models viz., KNN, SVM, ELM and random forest that revealed the superiority of the developed method.

Хеджирование в научной статье

The article focuses on linguistic hedging which helps avoid sounding arrogant or too certain of what you state in a research article. The relevance of the theme is based on the fact that nowadays the increasing number of Russian-speaking authors make attempts to publish their research articles in international journals. Writing research papers in the English language should follow some rules adopted in the English communicative culture. One of such rules is using hedging as a means of criticism mitigation and a “saving face” strategy. As hedging is not generally used in the Russian scientific discourse it is necessary to teach Russian scholars to apply hedging techniques in their writing. The aim of the article is to investigate the essence of hedging through analysis of sources written both in English and in Russian. The methods used include examination of Russian and English research papers, their comparative analysis, summing up recommendations on hedging in scientific discourse. As a result, the main lexical means of hedging are pointed out. A conclusion is made on necessity to include teaching linguistic hedging into courses of written academic communication for master degree students and postgraduates.

Multicriteria decision-making based on distance measures and knowledge measures of Fermatean fuzzy sets.

Fermatean fuzzy sets are more powerful than fuzzy sets, intuitionistic fuzzy sets, and Pythagorean fuzzy sets in handling various problems involving uncertainty. The distance measures in the fuzzy and non-standard fuzzy frameworks have got their applicability in various areas such as pattern analysis, clustering, medical diagnosis, etc. Also, the fuzzy and non-standard fuzzy knowledge measures have played a vital role in computing the criteria weights in the multicriteria decision-making problems. As there is no study concerning the distance and knowledge measures of Fermatean fuzzy sets, so in this paper, we propose some novel distance measures for Fermatean fuzzy sets using t-conorms. We also discuss their various desirable properties. With the help of suggested distance measures, we introduce some knowledge measures for Fermatean fuzzy sets. Through numerical comparison and linguistic hedges, we establish the effectiveness of the suggested distance measures and knowledge measures, respectively, over the existing measures in the Pythagorean/Fermatean fuzzy setting. At last, we demonstrate the application of the suggested measures in pattern analyis and multicriteria decision-making.

Applications of a picture fuzzy correlation coefficient in pattern analysis and decision-making.

Picture fuzzy set is an efficient tool for dealing with uncertainty and vagueness, particularly in situations that require assimilation of more dimensions of linguistic assessment such as human voting, feature selection, etc. The correlation coefficient of picture fuzzy sets is a tool to determine the association of two picture fuzzy sets. It has several applications in various disciplines like science, engineering, and management. The prominent applications include decision-making, pattern recognition, clustering analysis, medical diagnosis, etc. In this paper, we introduce a new correlation coefficient for picture fuzzy sets with the justification of its advantages. This correlation coefficient is better than the existing correlation coefficients and other such measures in the picture fuzzy theory because it considers the picture fuzzy set as a whole and also expresses the nature (positive or negative) as well as the extent of association between two PFSs. By performing some comparative analysis based on the computation of correlation degree and linguistic hedges, we establish the effectiveness of the suggested correlation measure over some available correlation measures in a picture fuzzy environment. Further, in the context of pattern recognition, we examine the performance of the proposed correlation measure over some existing picture fuzzy correlation measures. Finally, we apply the suggested picture fuzzy correlation coefficient to a decision-making problem involving the selection of an appropriate COVID-19 mask.

Effect of linguistic hedges on General Type-2 fuzzy representation of linear adjectives

This paper addresses the ‘modeling’ aspect of the computing with words (CWW) paradigm. The objective is to offer the computations for fuzzy modeling of phrases consisting of linear adjectives and linguistic hedges. The study conducted is novel in view that the effect of linguistic hedges on the Type-2 representation of the linear adjectives is investigated, particularly the Linear General Type-2 (LGT2) representation reported lately in the literature. Thus, the paper contributes to outline the General Type-2 representation of the phrases such as very tall, more or less short, etc. Particularly, the study finds application in the assignment of membership functions to the linguistic labels in complex fuzzy logic system which serves to complex CWW problems. The implementation carried out for the conducted study reports results that are in agreement with the effect caused by the linguistic hedges.

A unified approach to four important classes of unary operators

In this paper, we study operator dependent modifiers and we interpret the dual pair of modal operators based on an algebraic definition. It is a known fact that the substantiating and weakening modifier operators can be induced by repeating the arguments of conjunctive and disjunctive operators. We provide the conditions for which these modifier operators satisfy the requirements for a dual pair of necessity and possibility operators. Next, the necessary and sufficient condition for the distributivity of unary operators over conjunctive and disjunctive operators is presented. This also means that setting the distributivity as a requirement results in a unary operator that is identical to the modal operators mentioned above. Using this property, we establish an important connection between modal operators and linguistic hedges. Previously, we demonstrated that the unary operators induced by compositions of two strong negations satisfy the requirements for a dual pair of modal operators. Here, we view the negation operator as a modifier operator. Then, it is shown that (1) the strong negations, (2) the substantiating and weakening modifier operators, modal operators and linguistic hedges mentioned above, and (3) the unary operators, which are distributive over conjunctive and disjunctive operators, may be viewed as special cases of a unified unary operator class.

Лингвистическое хеджирование как средство реализации стратегий вежливости в разговорном диалоге

The paper presents the study of linguistic hedging in English spoken dialogue. It reviews the meaning of hedges and discusses their use within the framework of negative and positive politeness strategies. Politeness and hedging are interrelated linguistic phenomena that help maintain and regulate relationships between interlocutors and play an important role in harmonizing communication and achieving mutual understanding in various situations of interaction.

LEXICAL AND GRAMMATICAL HEDGING AS THE MEANS OF AUTHOR’S MODALITY

The article examines the phenomenon of linguistic hedging related to the establishment of theauthor’s responsibility limits for the statement and the mitigation of ultimacy.In English texts, hedging tools are used to express politeness to readers and interlocutors, to avoidsubjectivity in the information presentation and to protect themselves from possible criticism.Hedging as a subjective method of influence has an objective reflection in reality and acts as apromising direction to a successful communication strategy of political, scientific, public discourse.Non-ultimacy is realized through the use of so-called hedges, i.e. means belonging to differentlanguage levels. The article focuses on lexical and grammatical means: modal verbs, adjectives andnouns with the meaning of probability and possibility, quantitative nouns, adjectives and adverbs, aswell as inversions that emphasize that the author distances himself from the formulated conclusions,refers to the authority of others, refers to the authority of others. as probable, emphasizes that theopinion or conclusion does not belong to him personally.The article provides a structural, semantic and pragmatic analysis of hedge markers, as well asthe classification of hedging instruments by both structural and functional criteria.The study reviews the pragmatic causes and functional features of language tools marked by theperlocutionary intention of uncertainty. The study highlights basic lexical units belonging to thearsenal of hedging instruments. The results of the study can be seen as confirmation of the tendencyto increase the category of uncertainty in modern linguistic consciousness.