Linguistic Information Research Articles

Language comprehension and cognitive orientation switching in Brocas syndrome and Parkinsons disease

This paper reviews researches on sentence comprehension deficits and the cognitive orientation switching abilities in two neurological disorders Brocas aphasia and Parkinsons disease (PD). Both patient groups exhibit challenges in sentence comprehension. Individuals with Brocas aphasia show reduced lexical activation and struggle with processing syntactic information when comprehending sentences. While they may retain some comprehension abilities, complex grammatical processing is significantly hindered. In PD, sentence comprehension deficits arise from disruptions in attention, executive control, and action-language networks in the brain which are critical for integrating linguistic information. Regarding cognitive orientation switching, patients with both Brocas aphasia and PD demonstrate impairments, likely due to dysfunction in frontal brain regions that enable flexibility in thinking. Broca's patients struggle with changing response strategies, while PD patients have difficulty learning new classification rules and are challenged by distractions. There is evidence that reduced cognitive switching abilities can interfere with effective syntactic processing during sentence comprehension. The review highlights neurological underpinnings and manifestations of comprehension and cognitive flexibility deficits in these two disorders. Further research can provide greater insight into the nuanced interactions between language and domain-general cognition.

Justice must be seen to be done: a multimodal attitude analysis of attorneys’ closing arguments

Abstract Multimodal discourse analysis offers a novel lens for the study of legal discourse. Within the closing arguments of a trial, prosecution and defense attorneys utilize various multimodal resources to present evidence, articulate opinions, and adopt stances to achieve their communicative goals. This research focuses on the closing arguments in the criminal trial concerning the death of George Floyd to analyze the multimodal representation of the closing arguments delivered by both prosecution and defense attorneys. It employs the analytical framework of the attitude system and paralinguistic typology proposed by Martin and colleagues, and uses the UAM Corpus Tool and Praat to qualitatively annotate and quantify the linguistic and paralinguistic information. The findings reveal that these attorneys employ different multimodal resources, including language and paralanguage, to convey their attitudes towards the case’s participants. This leads to the construction of competing narratives, shedding light on how justice is seen to be done.

The reading brain extracts syntactic information from multiple words within 50 milliseconds

To what extent do readers process multiple words in parallel? Although it is now commonly accepted that letters are processed across multiple words simultaneously, higher-order (lexical, semantic, syntactic) parallel processing remains contentious. Recent use of the flanker paradigm has revealed that the syntactic recognition of foveal target words is influenced by the syntactic congruency of parafoveal flanking words even when target and flankers are shown for only 170 ms. It has been argued, however, that such settings may allow processing of multiple words even if this were to happen on a serial one-by-one basis. To circumvent this possibility, here I have tested participants in a syntactic categorization task whereby targets and flankers were shown for only 50 ms and replaced by post-masks. Significant effects of target-flanker congruency were observed in both response times and accuracy, indicating that readers extracted syntactic information from multiple words within the very brief presentation time. The present results strongly suggest that the brain extracts higher-order linguistic information from multiple words in parallel.

ИНТЕГРАЦИЯ ОБУЧЕНИЯ АНГЛИЙСКОМУ ЯЗЫКУ И ФОРМИРОВАНИЯ КРИТИЧЕСКОГО МЫШЛЕНИЯ В УЧЕБНО-МЕТОДИЧЕСКОМ ПОСОБИИ

This article deals with the issues to develop critical thinking in teaching a foreign language. It gives the experience of writing a textbook to study English for university students through critical thinking development. It shows the modern challenge to work with information, to select it, percept, analyze, assess, and judge. It considers critical thinking as an integral component of information literacy of the personality which we need to develop purposefully and systematically in the educational process. The authors come to a conclusion that it is possible to integrate effectively the purposes to develop critical thinking of the personality in particular and information literacy in general into the process of teaching a foreign language. It provides the proof that we can emphasize critical thinking development of the personality at English classes studying different lexical and grammatical topics. It also studies a step-by-step algorithm to develop the desired personality skills within several lexical topics, illustrating it with different types of exercises aimed to form the linguistic competence and information literacy of the student's personality. It uses the methods of theoretical and practical analyses of the problem to develop critical thinking in the educational process. The positive experience of introducing critical thinking into the English language learning proves the potential and prospect of such an integration.

A fuzzy group decision-making framework for computer network security evaluation with probabilistic linguistic information

The computer network security evaluation is a classical multiple attribute group decision making (MAGDM) problems. Recently, the Exponential TODIM TODIM (ExpTODIM) method has been used to cope with MAGDM issues. The probabilistic linguistic term sets (PLTSs) are used as a tool for characterizing uncertain information during the computer network security evaluation. In this manuscript, the probabilistic linguistic ExpTODIM (PL-ExpTODIM) is built to solve the MAGDM under PLTSs. In the end, a numerical case study for computer network security evaluation is given to validate the proposed method. The main contribution of this paper is constructed: (1) the Exponential TODIM TODIM (ExpTODIM) method is extended to the PLTSs; (2) the probabilistic linguistic ExpTODIM (PL-ExpTODIM) method is defined to solve the MAGDM under PLTSs; (3) In the end, a numerical case study for computer network security evaluation is given to validate the proposed method.

Predictions about prosody facilitate lexical access: Evidence from P50/N100 and MMN components

Research into the neural foundation of perception asserts a model where top-down predictions modulate the bottom-up processing of sensory input. Despite becoming increasingly influential in cognitive neuroscience, the precise account of this predictive coding framework remains debated. In this study, we aim to contribute to this debate by investigating how predictions about prosody facilitate speech perception, and to shed light especially on lexical access influenced by simultaneous predictions in different domains, inter alia, prosodic and semantic. Using a passive auditory oddball paradigm, we examined neural responses to prosodic changes, leading to a semantic change as in Dutch nouns canon [ˈkaːnɔn] ‘canon’ vs kanon [kaːˈnɔn] ‘cannon’, and used acoustically identical pseudowords as controls. Results from twenty-eight native speakers of Dutch (age range 18–32 years) indicated an enhanced P50/N100 complex to prosodic change in pseudowords as well as an MMN response to both words and pseudowords. The enhanced P50/N100 response to pseudowords is claimed to indicate that all relevant auditory information is still processed by the brain, whereas the reduced response to words might reflect the suppression of information that has already been encoded. The MMN response to pseudowords and words, on the other hand, is best justified by the unification of previously established prosodic representations with sensory and semantic input respectively. This pattern of results is in line with the predictive coding framework acting on multiple levels and is of crucial importance to indicate that predictions about linguistic prosodic information are utilized by the brain as early as 50 ms.

Group decision-making method based on advanced aggregation operators with entropy and divergence measures under 2-tuple linguistic Pythagorean fuzzy environment

2-tuple linguistic Pythagorean fuzzy values (2TLPFVs) are flexible and more efficient tools to represent fuzzy linguistic information in complex real-world situations than 2-tuple linguistic values (2TLVs) and 2-tuple linguistic intuitionistic fuzzy values (2TLIFVs). This paper introduces a new score value function for 2TLPFVs and discusses its major properties. Two new aggregation operators (AOs) are presented, namely, the advanced 2-tuple linguistic Pythagorean fuzzy weighted average (Adv-2TLPFWA) operator and the advanced 2-tuple linguistic Pythagorean fuzzy weighted geometric (Adv-2TLPFWG) operator, which aggregate 2-tuple linguistic Pythagorean fuzzy information based on novel operational laws of 2TLPFVs. The work also exhibits several properties of these AOs in detail. Furthermore, the article discusses entropy and divergence measures under the 2-tuple linguistic Pythagorean fuzzy information environment. Finally, a new multiple attribute group decision-making (MAGDM) method is constructed to solve group decision-making problems in the 2-tuple linguistic Pythagorean fuzzy context. A real-life group decision-making problem of selecting the best green supplier for fruits and vegetables is investigated to verify the applicability and validity of the proposed approach. The sensitivity investigation and comparative analysis are conducted to illustrate the advantages of the proposed method.

Social network trust relationship environment based advanced ovarian cancer treatment decision-making model: An approach based on linguistic information with experts’ multiple confidence levels

Advanced ovarian cancer is the deadliest gynecologic cancer in World. The survival rate is not significant, and it depends on how well cancer responds to treatment. Generally, the treatment decision depends on Gynecologic oncology experts, which treatment is best for the patient. However, a Gynecologic oncology expert’s limited rationality and personality traits strongly impact a patient’s treatment decision. In addition, according to the American Cancer Society, a patient’s treatment decision may change if Gynecologic oncology experts change. To solve this kind of medical problem, we design a group decision-making model (GDM) based on linguistic preference relations (LPRs) with multiple confidence levels like confidence and doubting levels of experts to analyse experts’ treatment decision opinions digitally for improving the survival rate of patients. In a GDM, consistency analysis and obtaining expert weights are two keys. Hence, this paper design an LPRs with confidence and doubting levels (LPRs-CDLs) based GDM model under a unique consistency improvement algorithm and Pythagorean linguistic trust relations social network analysis driven expert weight determination. First, we define the additive consistency of LPRs-CDLs based on minimum confidence and maximum doubting levels. Second, a consistency reaching process (CRP) algorithm with convergence theorem is offered to improve the consistency of experts’ opinion decisions. Third, develop a trust propagation model based on Pythagorean linguistic trust relations under a social network environment for expert’s weights determination. Then, a social network trust relationship-based GDM model with LPRs-CDLs information is presented to the advanced ovarian cancer patient’s treatment final decision. Finally, we show the rationality of this study with some comparative analysis.

Studying the role of English and American literature in the dissemination of digital health concepts

Abstract This paper first analyzes the implementation of digital health education in English and American literature descriptively, based on the principles and characteristics of language art in English and American literature. Then, based on the word vector model, the CBOW model and Glove model are proposed for assigning weights to words with different occurrence frequencies, followed by linear splicing of word vectors and their additional feature vectors in sentences into final word vectors fed into the semantic analysis model, thus eliminating the phenomenon of illegal labels being predicted, improving the accuracy of semantic analysis prediction, and empirically analyzing English and American literary works under the concept of digital health analysis. The results showed that on digital health, the mean value of students’ evaluation of their mental health status in class 4 increased from 2.88 in the pre-test to 3.48 in the post-test, and the overall evaluation scores of mental health status were on an upward trend. For the model performance analysis, AvgPool’s summation and averaging of all features in the pooling domain can retain more linguistic information and reduce the training parameters to improve the model performance further. This study explores the concept of digital health in English and American literature to improve one’s cultural literacy.

LEXICAL AND GRAMMATICAL MARKUP OF TEXTS OF THE HISTORICAL SUBCORPUS

The base of a certain national corpus is not limited to information in the form of meta descriptions and metadata of entered texts. It is also necessary to develop linguistic markup. Linguistic markup is a linguistic information that is cataractarized to each lexical unit in the text according to spelling, phonetic, lexical, grammatical features. However, the development of the linguistic markup of the historical subcorpus is one of the complex tasks that require in-depth research. This is due to the fact that most of the texts included in the historical subcorpus are written in Arabic graphics. And the texts of the Middle Ages written in Arabic graphics were transcribed in different ways. The development of a historical subcorpus has difficulties both theoretically and technically, compared with other subcorpuses. In this regard, the purpose of the article is to consider the issue of linguistic markings for the texts of the historical subcorpus, which are being developed for the first time at the Institute of Linguistics named after Akhmet Baitursynula. Tasks: to identify linguistic, lexical and grammatical markup for transcribed texts; to take into account the experiences of other countries in the development of lexical and grammatical markup; to analyze transcribed texts from Arabic graphics to Cyrillic graphics; to identify the variability of transcribed words; to describe the mechanism of functioning of the lexical and grammatical markup program.The study uses descriptive, historical-comparative, linguotextological, linguostatistical methods. As a result of the study, when developing the markup, the experiments of the development of the historical subcorpus of the Russian language were considered; transcribing texts written in Arabic graphics of different periods of the Middle Ages were anasized; lexical and grammatical markup for transcribed texts were determined; the mechanisms of a lexical and grammatical search system for transcribed texts were described.Practical significance. The development of lexical and grammatical markup for transcribed texts included in the historical subcorpus will be a useful linguistic tool for studying the evolution of a certain lexical unit.

EXPERIENCE OF CREATING THE ONOMASTIC SUBCORPUS OF THE NATIONAL CORPUS OF THE KAZAKH LANGUAGE

The onomastic subcorpus is relevant for a number of sciences, especially for linguistics, geography, and cognitive science. The basic basis for the development of the subcorpus was the defended dissertations and scientific monographs and works on Kazakh onomastics. The article provides an overview of the history of the development of Kazakh onomastics and discusses the role and significance of the creation of the onomastic subcorpus. The types of onoms included in the subcorpus and the characteristic features attributed to them are discussed. The article points out that the onomastic subcorpus includes linguistic information and metainformation of proper names. The onomastic subcorpus can be used both for scientific and practical purposes. First, the content and functions of proper names found in texts of different genres are revealed. On the other hand, this subcorpus as a comprehensive dictionary provides the user with comprehensive information about the geolocation of the toponymic object, place of birth, name, flora and fauna, etc. The main purpose of the onomastic subcorpus is to systematize a variety of scientific, cultural information that is contained in the onomastic name. Proper names are a social phenomenon, closely connected with the life of society, due to which its semantics is often dominated by ideological load. Each onym refers to a certain period and preserves its historical character. Therefore, it contains non-linguistic information, namely geographical, ethno-cultural, and historical information. The task of the onomastic corpus is to collect, systematize, and make this information publicly available in electronic form. The proper names included in the corpus are characterized in terms of word formation, grammatical regularities, as well as in terms of semantic, etymological, orthographic, etc.

Optimization models of consensus measurement and improvement processes with hesitant fuzzy linguistic evaluation information

Optimization models of consensus measurement and improvement processes with hesitant fuzzy linguistic evaluation information

A novel multi-interval-valued fuzzy set model to solve MADM problems

This paper introduces a new Multi-interval-valued fuzzy sets (MIVFSs) model, in which the membership degrees of an alternative to given a set are represented by multiple interval values within the [0, 1] range along with their respective counting numbers. Then, the related concepts, order strategies, and some basic operational rules of multi-interval-valued fuzzy elements are investigated. Afterwards, several information measures including distance, similarity, and interval projection are further developed. Building upon this foundation, we designed two multiple-attribute decision-making (MADM) ranking methods in the multi-interval-valued fuzzy information system. To illustrate the practical application of our model, we provide a numerical example involving linguistic information, addressing the issue of ranking doctors in online healthcare. Through a comparative analysis, we demonstrate the effectiveness and practicality of the proposed model in handling MADM problems.

Multi-criteria group decision-making methods with dynamic probabilistic linguistic information characterized by multiple consecutive time points

Multi-criteria group decision-making methods with dynamic probabilistic linguistic information characterized by multiple consecutive time points

Incorporating Corpus Linguistics Tools in the Training and Professional Development of Lecturers in Translation Studies

This research explores the impacts and strategies for integrating corpus linguistics tools in training translation lecturers. The focus of the study is to evaluate how the integration of corpus tools in the training of translation lecturers can enhance their efficiency and professional development. Data was collected through the use of questionnaires from 114 lecturers in translation studies and Ph.D. researchers in translation. Analysis was carried out using relevant statistical tools. The research findings indicate that the use of concordance tools, such as AntConc, has a notable positive impact on the effectiveness of translation instructors in demonstrating language patterns to their students. The substantial levels of agreement highlight the potential of these tools in facilitating the integration of theory and practice in the field of translation education. Furthermore, the participants recognized the effectiveness of specialized corpus-based translation tools such as TransTool in providing domain-specific linguistic information for accurate translation decision-making. The use of corpus-based tasks and exercises has developed as a strategic approach that strongly resonates with translation instructors, facilitating a comprehensive comprehension of the integration of corpus linguistics and its significance for educational methodologies. The aforementioned result underscores the need to include experiential learning and practical involvement to improve the effectiveness of translation teaching. However, there was a varied opinion of the influence of web-based corpora, suggesting the need for a more nuanced comprehension of their advantages and difficulties within the framework of translation education. Following the findings, it is thus concluded that the integration of corpus linguistics mechanisms in the training of translation lecturers remains effective as they enhance the use of real-world data in their translation studies and the use of data generated from a wide spectrum of languages in teaching translation.

Intuitionistic uncertain linguistic prioritized aggregation operators and their application to multi-criteria decision making

The intuitionistic uncertain linguistic information aggregation problems considering different priority levels of criteria are investigated. Firstly, we extended the prioritized averaging (PA) operator to intuitionistic uncertain linguistic environment, defined two new prioritized aggregation operators called the intuitionistic uncertain linguistic prioritized weighted average (IULPWA) operator and the intuitionistic uncertain linguistic prioritized weighted geometric (IULPWG) operator, and established various desirable properties of the proposed operators. Secondly, we developed a multi-criteria decision making (MCDM) approach based on the IULPWA operator (or the IULPWG operator) to deal with the MCDM problems in which the criterion values take the form of intuitionistic uncertain linguistic numbers (IULNs) and the criteria are in different priority levels. Finally, an example is given to illustrate the feasibility and effectiveness of the proposed method, and a comparison analysis is conducted to make clear the differences among the IULPWA operator, the IULPWG operator, the intuitionistic uncertain linguistic number weighted averaging (IULNWA) operator and the intuitionistic uncertain linguistic weighted geometric average (IULWGA) operator.

A statistical approach to large-scale group decision making with incomplete hesitant fuzzy linguistic information by incorporating risk attitude

In response to the lack of information and the arguments among the decision-makers (DMs) in large-scale group decision making (LSGDM), this study proposes a method that integrates decision risk and risk attitude into LSGDM with hesitant fuzzy linguistic preference relations (HFLPRs) based on statistical estimation. Firstly, the DMs are arranged into several subgroups via their risk attitudes. Next, the whole or part of the five-number summary for subgroups from incomplete information provided by DMs are collected. After that, the optimization model and Monte Carlo simulation are utilized to obtain mean and standard deviation estimators of the decision information, and the mean estimator is regarded as the evaluation information of subgroup. Then, the hesitant fuzzy linguistic term sets (HFLTSs) with different number of linguistic variables are normalized according to their risk attitudes and confidence interval. Meanwhile, the number of members in subgroups and the decision risk level of subgroups are combined to determine the weights of subgroups. Moreover, a minimum deviation model is derived to rank alternatives. Finally, a case study, sensitivity analysis and comparison are used to guarantee the rationality and availability of the proposed method.

A multi-attribute decision-making framework for enterprise competitive intelligence system evaluation with 2-tuple linguistic neutrosophic information

Big data is changing our lives and the way we understand the world, as well as the operational patterns of business and social organizations. Fully understanding the value of data and knowing how to use big data to provide a basis for business decision-making has gradually become the most basic thinking that business organizations should possess in the era of big data. Under the thinking mode of data-driven decision-making, many information science researchers have discussed the model, architecture, operation mechanism and other aspects of big data competitive intelligence system. At the same time, more and more enterprises, such as IBM, Amazon, Google, Microsoft, Wal Mart, etc., have begun to attach importance to the development and construction of big data competitive intelligence software systems, and have achieved certain results. The enterprise competitive intelligence system evaluation in the context of big data is regarded as multi-attribute decision-making (MADM). In this paper, the Hamy mean (HM) and the power average (PA) are connected with 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the 2-tuple linguistic neutrosophic numbers power HM (2TLNPHM) operator. Then, use the 2TLNPHM operator to handle MADM with 2TLNS. Finally, taking the enterprise competitive intelligence system evaluation in the context of big data as an example, the proposed method is explained. The main contributions of this study are summarized: the establishment of the 2TLNPHM operator; (2) The 2TLNPHM operator was developed to handle MADM with 2TLNS; (3) Through the empirical application of the enterprise competitive intelligence system evaluation, the proposed method is validated; (4) Some comparative studies have shown the rationality of the 2TLNPHM operator.

Dissociating the pre-activation of word meaning and form during sentence comprehension: Evidence from EEG representational similarity analysis.

During language comprehension, the processing of each incoming word is facilitated in proportion to its predictability. Here, we asked whether anticipated upcoming linguistic information is actually pre-activated before new bottom-up input becomes available, and if so, whether this pre-activation is limited to the level of semantic features, or whether extends to representations of individual word-forms (orthography/phonology). We carried out Representational Similarity Analysis on EEG data while participants read highly constraining sentences. Prior to the onset of the expected target words, sentence pairs predicting semantically related words (financial "bank" - "loan") and form-related words (financial "bank" - river "bank") produced more similar neural patterns than pairs predicting unrelated words ("bank" - "lesson"). This provides direct neural evidence for item-specific semantic and form predictive pre-activation. Moreover, the semantic pre-activation effect preceded the form pre-activation effect, suggesting that top-down pre-activation is propagated from higher to lower levels of the linguistic hierarchy over time.

Fully and Weakly Supervised Referring Expression Segmentation With End-to-End Learning

Referring Expression Segmentation (RES), which is aimed at localizing and segmenting the target according to the given language expression, has drawn increasing attention. Existing methods jointly consider the localization and segmentation steps, which rely on the fused visual and linguistic features for both steps. We argue that the conflict between the purpose of identifying an object and generating a mask limits the RES performance. To solve this problem, we propose a parallel position-kernel-segmentation pipeline to better isolate and then interact the localization and segmentation steps. In our pipeline, linguistic information will not directly contaminate the visual feature for segmentation. Specifically, the localization step localizes the target object in the image based on the referring expression, and then the visual kernel obtained from the localization step guides the segmentation step. This pipeline also enables us to train RES in a weakly-supervised way, where the pixel-level segmentation labels are replaced by click annotations on center and corner points. The position head is fully-supervised and trained with the click annotations as supervision, and the segmentation head is trained with weakly-supervised segmentation losses. To validate our framework on a weakly-supervised setting, we annotated three RES benchmark datasets (RefCOCO, RefCOCO+ and RefCOCOg) with click annotations. Our method is simple but surprisingly effective, outperforming all previous state-of-the-art RES methods on fully- and weakly-supervised settings by a large margin. The benchmark code and datasets will be released.