One Cue's Loss Is Another Cue's Gain-Learning Morphophonology Through Unlearning.

A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney, Kügler, & van de Vijver, 2015; Baer-Henney & van de Vijver, 2012), error-driven learning suggests that contingency rather than contiguity is crucial (Nixon, 2020; Ramscar, Yarlett, Dye, Denny, & Thorpe, 2010). In error-driven learning, cues gain association strength if they predict a certain outcome, and they lose strength if the outcome is absent. This reduction of association strength is called unlearning. So far, it is unclear if such unlearning has consequences for cue-outcome associations beyond the ones that get reduced. To test for such consequences of unlearning, we taught participants morphophonological patterns in an artificial language learning experiment. In one block, the cues to two morphological outcomes-plural and diminutive-co-occurred within the same word forms. In another block, a single cue to only one of these two outcomes was presented in a different set of word forms. We wanted to find out, if participants unlearn this cue's association with the outcome that is not predicted by the cue alone, and if this allows the absent cue to be associated with the absent outcome. Our results show that if unlearning was possible, participants learned that the absent cue predicts the absent outcome better than if no unlearning was possible. This effect was stronger if the unlearned cue was more salient. This shows that unlearning takes place even if no alternative cues to an absent outcome are provided, which highlights that learners take both positive and negative evidence into account-as predicted by domain general error-driven learning.

An integrated group decision-making method under q-rung orthopair fuzzy 2-tuple linguistic context with partial weight information.

Considering the advantages of q-rung orthopair fuzzy 2-tuple linguistic set (q-RFLS), which includes both linguistic and numeric data to describe evaluations, this article aims to design a new decision-making methodology by integrating Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and qualitative flexible (QUALIFLEX) methods based on the revised aggregation operators to solve multiple criteria group decision making (MCGDM). To accomplish this, we first revise the extant operational laws of q-RFLSs to make up for their shortcomings. Based on novel operational laws, we develop q-rung orthopair fuzzy 2-tuple linguistic (q-RFL) weighted averaging and geometric operators and provide the corresponding results. Next, we develop a maximization deviation model to determine the criterion weights in the decision-making procedure, which accounts for partial weight unknown information. Then, the VIKOR and QUALIFLEX methodologies are combined, which can assess the concordance index of each ranking combination using group utility and individual maximum regret value of alternative and acquire the ranking result based on each permutation's general concordance index values. Consequently, a case study is conducted to select the best bike-sharing recycling supplier utilizing the suggested VIKOR-QUALIFLEX MCGDM method, demonstrating the method's applicability and availability. Finally, through sensitivity and comparative analysis, the validity and superiority of the proposed method are demonstrated.

Using Large Language Models to Understand Suicidality in a Social Media-Based Taxonomy of Mental Health Disorders: Linguistic Analysis of Reddit Posts.

Background: Rates of suicide have increased by over 35% since 1999. Despite concerted efforts, our ability to predict, explain, or treat suicide risk has not significantly improved over the past 50 years. Objective: The aim of this study was to use large language models to understand natural language use during public web-based discussions (on Reddit) around topics related to suicidality. Methods: We used large language model-based sentence embedding to extract the latent linguistic dimensions of user postings derived from several mental health-related subreddits, with a focus on suicidality. We then applied dimensionality reduction to these sentence embeddings, allowing them to be summarized and visualized in a lower-dimensional Euclidean space for further downstream analyses. We analyzed 2.9 million posts extracted from 30 subreddits, including r/SuicideWatch, between October 1 and December 31, 2022, and the same period in 2010. Results: Our results showed that, in line with existing theories of suicide, posters in the suicidality community (r/SuicideWatch) predominantly wrote about feelings of disconnection, burdensomeness, hopeless, desperation, resignation, and trauma. Further, we identified distinct latent linguistic dimensions (well-being, seeking support, and severity of distress) among all mental health subreddits, and many of the resulting subreddit clusters were in line with a statistically driven diagnostic classification system-namely, the Hierarchical Taxonomy of Psychopathology (HiTOP)-by mapping onto the proposed superspectra. Conclusions: Overall, our findings provide data-driven support for several language-based theories of suicide, as well as dimensional classification systems for mental health disorders. Ultimately, this novel combination of natural language processing techniques can assist researchers in gaining deeper insights about emotions and experiences shared on the web and may aid in the validation and refutation of different mental health theories.

The morpho-syntax of question particles in Standard Arabic.

Unlike wh-question questions in Standard Arabic (SA), which received much attention in the past decades in different approaches within generative grammar, question particles (yes-no questions) in SA have not yet been studied thoroughly in minimalist syntax, and less attention has been paid to them. There is a need to analyze SA question articles and explore their syntactic behavior within minimalism. The reason why this topic has been selected for study is that SA question particles have not been investigated in detail yet in Chomsky's Phase Theory; it has not been analyzed how question particles are derived and represented morpho-syntactically in a clause structure. Therefore, this study aims to investigate the morpho-syntax of SA question particles and provide satisfactory answers to the following questions: (i) Do question particles in SA undergo any syntactic movement to [Spec-CP] in the derivation of yes-no questions? If not, why?, (ii) Are question particles based-generated in [Spec-CP]?, and (iii) How can question particles be accounted for neatly in Chomsky's Phase-based Theory? The paper adopts Chomsky's Phase Theory to examine the interaction between the assumptions of this theory and the SA data on question particles. The study findings reveal that, unlike English, question particles in SA do not undergo any syntactic movement while deriving yes-no questions and are assumed to be base-generated in [Spec-CP]. Such question particles are not part of the verb morphology and are merely morphological affixes used as devices to mark interrogativity in the syntax; they do not carry any agreement and tense features that trigger syntactic movement to the clause-initial position.

Global musical diversity is largely independent of linguistic and genetic histories.

Music is a universal yet diverse cultural trait transmitted between generations. The extent to which global musical diversity traces cultural and demographic history, however, is unresolved. Using a global musical dataset of 5242 songs from 719 societies, we identify five axes of musical diversity and show that music contains geographical and historical structures analogous to linguistic and genetic diversity. After creating a matched dataset of musical, genetic, and linguistic data spanning 121 societies containing 981 songs, 1296 individual genetic profiles, and 121 languages, we show that global musical similarities are only weakly and inconsistently related to linguistic or genetic histories, with some regional exceptions such as within Southeast Asia and sub-Saharan Africa. Our results suggest that global musical traditions are largely distinct from some non-musical aspects of human history.

How to choose a design thinking method for teaching the design of localization: A two-dimension linguistic fuzzy model with two-tuples.

There are many different types of scientific design thinking methods, but it is necessary to evaluate the applicability of the methods to the components of the design teaching curriculum in universities. Therefore, this study assesses the applicability of design thinking in terms of "design practice" and "locality" based on the local design education philosophy and the characteristics of the students and courses. A two-dimensional linguistic fuzzy model with two-tuples was proposed, and the assessment values of 36 experts were statistically analysed using the Delphi, triangular fuzzy number, Euclidean distance, two-dimension linguistic label (2DLL), and two-dimensional linguistic weighted arithmetic aggregation (2DLWAA) methods. The results highlighted the 12 categories of design thinking methods that are most applicable to teaching and learning, indicating the basic views of university design faculty on the application of design thinking methods. Finally, the new design teaching methods have been validated and constructed through years of teaching practice, and have some reference value for teaching design courses in universities.

Negation mitigates rather than inverts the neural representations of adjectives.

Combinatoric linguistic operations underpin human language processes, but how meaning is composed and refined in the mind of the reader is not well understood. We address this puzzle by exploiting the ubiquitous function of negation. We track the online effects of negation ("not") and intensifiers ("really") on the representation of scalar adjectives (e.g., "good") in parametrically designed behavioral and neurophysiological (MEG) experiments. The behavioral data show that participants first interpret negated adjectives as affirmative and later modify their interpretation towards, but never exactly as, the opposite meaning. Decoding analyses of neural activity further reveal significant above chance decoding accuracy for negated adjectives within 600 ms from adjective onset, suggesting that negation does not invert the representation of adjectives (i.e., "not bad" represented as "good"); furthermore, decoding accuracy for negated adjectives is found to be significantly lower than that for affirmative adjectives. Overall, these results suggest that negation mitigates rather than inverts the neural representations of adjectives. This putative suppression mechanism of negation is supported by increased synchronization of beta-band neural activity in sensorimotor areas. The analysis of negation provides a steppingstone to understand how the human brain represents changes of meaning over time.

Linguistic, Content and Face Validity of the Swedish Version of a Quality-of-Life Assessment for Children, Teenagers and Adults with Spina Bifida.

Spina bifida includes a spectrum of different neural tube defects. Myelomeningocele is the most serious type and is associated with a risk of paralysis and sensory dysfunction below the affected level, bladder/bowel dysfunction, brain dysmorphology, and impaired health-related quality of life (HRQoL). The aim of this study was to describe the establishment of linguistic, content and face validity of the Swedish version of a Quality-of-Life Assessment for children (QUALAS-C, n = 10 items), teenagers (QUALAS-T, n = 10 items) and adults with spina bifida (QUALAS-A, n = 15 items) based on the original US English versions. The process included close collaboration with the original instrument developer and complied with international standards on patient-reported outcome measurements. The procedure includes forward translation, expert and patient/parent review and reconciliation, back translation, back translation review and cognitive debriefing interviews with 16 people with spina bifida aged 8 to 33, providing them with the possibility of evaluating the clarity, adequacy, and comprehensiveness of QUALAS-C, QUALAS-T and QUALAS-A, respectively. The interviews lasted a median of 15 min (range 8-16) for QUALAS-C, 10 min (range 9-15) for QUALAS-T and 24 min (range 9-38) for QUALAS-A. Four main issues/topics needed attention and discussion after both the forward and back translation. Following the back translation review, all issues were resolved. The patient feedback revealed recognition of the HRQoL issues included in QUALAS, and also difficulties in understanding some questions. After the patients' evaluation, four items were reworded for clarity. No study participant reported a wish to add to or remove questions from QUALAS. Hence, the Swedish versions of QUALAS became conceptually equivalent to the original US English versions and achieved linguistic, content and face validity. While empowering the voices of people with spina bifida, these results also enable their HRQoL to be properly assessed in research and clinical care in Sweden and in international studies.

Perception of cure in prostate cancer: human-led and artificial intelligence-assisted landscape review and linguistic analysis of literature, social media and policy documents.

BACKGROUND: Understanding stakeholders' perception of cure in prostate cancer (PC) is essential to preparing for effective communication about emerging treatments with curative intent. This study used artificial intelligence (AI) for landscape review and linguistic analysis of definition, context and value of cure among stakeholders in PC. MATERIALS AND METHODS: Subject-matter experts (SMEs) selected cure-related key words using Elicit, a semantic literature search engine, and extracted hits containing the key words from Medline, Sermo and Overton, representing academic researchers, health care providers (HCPs) and policymakers, respectively. NetBase Quid, a social media analytics and natural language processing tool, was used to carry out key word searches in social media (representing the general public). NetBase Quid analysed linguistics of key word-specific hit sets for key word count, geolocation and sentiments. SMEs qualitatively summarised key word-specific insights. Contextual terms frequently occurring with key words were identified and quantified. RESULTS: SMEs identified seven key words applicable to PC (number of acquired hits) across four platforms: Cure (12429), Survivor (6063), Remission (1904), Survivorship (1179), Curative intent (432), No evidence of disease (381) and Complete remission (83). Most commonly used key words were Cure by the general public and HCPs (11815 and 224 hits), Survivorship by academic researchers and Survivor by policymakers (378 hits each). All stakeholders discussed Cure and cure-related key words primarily in early-stage PC and associated them with positive sentiments. All stakeholders defined cure differently but communicated about it in relation to disease measurements (e.g. prostate-specific antigen) or surgery. Stakeholders preferred different terms when discussing cure in PC: Cure (academic researchers), Cure rates (HCPs), Potential cure and Survivor/Survivorship (policymakers) and Cure and Survivor (general public). CONCLUSION: This human-led, AI-assisted large-scale qualitative language-based research revealed that cure was commonly discussed by academic researchers, HCPs, policymakers and the general public, especially in early-stage PC. Stakeholders defined and contextualised cure in their communications differently and associated it with positive value.

Linguistic-based Mild Cognitive Impairment detection using Informative Loss.

This paper presents a deep learning method using Natural Language Processing (NLP) techniques, to distinguish between Mild Cognitive Impairment (MCI) and Normal Cognitive (NC) conditions in older adults. We propose a framework that analyzes transcripts generated from video interviews collected within the I-CONECT study project, a randomized controlled trial aimed at improving cognitive functions through video chats. Our proposed NLP framework consists of two Transformer-based modules, namely Sentence Embedding (SE) and Sentence Cross Attention (SCA). First, the SE module captures contextual relationships between words within each sentence. Subsequently, the SCA module extracts temporal features from a sequence of sentences. This feature is then used by a Multi-Layer Perceptron (MLP) for the classification of subjects into MCI or NC. To build a robust model, we propose a novel loss function, called InfoLoss, that considers the reduction in entropy by observing each sequence of sentences to ultimately enhance the classification accuracy. The results of our comprehensive model evaluation using the I-CONECT dataset show that our framework can distinguish between MCI and NC with an average area under the curve of 84.75%.

How Informal Carers Support Video Consulting in Physiotherapy, Heart Failure, and Cancer: Qualitative Study Using Linguistic Ethnography.

BACKGROUND: Informal carers play an important role in the everyday care of patients and the delivery of health care services. They aid patients in transportation to and from appointments, and they provide assistance during the appointments (eg, answering questions on the patient's behalf). Video consultations are often seen as a way of providing patients with easier access to care. However, few studies have considered how this affects the role of informal carers and how they are needed to make video consultations safe and feasible. OBJECTIVE: This study aims to identify how informal carers, usually friends or family who provide unpaid assistance, support patients and clinicians during video consultations. METHODS: We conducted an in-depth analysis of the communication in a sample of video consultations drawn from 7 clinical settings across 4 National Health Service Trusts in the United Kingdom. The data set consisted of 52 video consultation recordings (of patients with diabetes, gestational diabetes, cancer, heart failure, orthopedic problems, long-term pain, and neuromuscular rehabilitation) and interviews with all participants involved in these consultations. Using Linguistic Ethnography, which embeds detailed analysis of verbal and nonverbal communication in the context of the interaction, we examined the interactional, technological, and clinical work carers did to facilitate video consultations and help patients and clinicians overcome challenges of the remote and video-mediated context. RESULTS: Most patients (40/52, 77%) participated in the video consultation without support from an informal carer. Only 23% (12/52) of the consultations involved an informal carer. In addition to facilitating the clinical interaction (eg, answering questions on behalf of the patient), we identified 3 types of work that informal carers did: facilitating the use of technology; addressing problems when the patient could not hear or understand the clinician; and assisting with physical examinations, acting as the eyes, ears, and hands of the clinician. Carers often stayed in the background, monitoring the consultation to identify situations where they might be needed. In doing so, copresent carers reassured patients and helped them conduct the activities that make up a consultation. However, carers did not necessarily help patients solve all the challenges of a video consultation (eg, aiming the camera while laying hands on the patient during an examination). We compared cases where an informal carer was copresent with cases where the patient was alone, which showed that carers provided an important safety net, particularly for patients who were frail and experienced mobility difficulties. CONCLUSIONS: Informal carers play a critical role in making video consultations safe and feasible, particularly for patients with limited technological experience or complex needs. Guidance and research on video consulting need to consider the availability and work done by informal carers and how they can be supported in providing patients access to digital health care services.

Analyzing the impact of unemployment on mental health among Chinese university graduates: a study of emotional and linguistic patterns on Weibo.

Purpose: This study explores the intricate relationship between unemployment rates and emotional responses among Chinese university graduates, analyzing how these factors correlate with specific linguistic features on the popular social media platform Sina Weibo. The goal is to uncover patterns that elucidate the psychological and emotional dimensions of unemployment challenges among this demographic. Methods: The analysis utilized a dataset of 30,540 Sina Weibo posts containing specific keywords related to unemployment and anxiety, collected from January 2019 to June 2023. The posts were pre-processed to eliminate noise and refine the data quality. Linear regression and textual analyses were employed to identify correlations between unemployment rates for individuals aged 16-24 and the linguistic characteristics of the posts. Results: The study found significant fluctuations in urban youth unemployment rates, peaking at 21.3% in June 2023. A corresponding increase in anxiety-related expressions was noted in the social media posts, with peak expressions aligning with high unemployment rates. Linguistic analysis revealed that the category of "Affect" showed a strong positive correlation with unemployment rates, indicating increased emotional expression alongside rising unemployment. Other categories such as "Negative emotion" and "Sadness" also showed significant correlations, highlighting a robust relationship between economic challenges and emotional distress. Conclusion: The findings underscore the profound impact of unemployment on the emotional well-being of university students, suggesting that economic hardships are closely linked to psychological stress and heightened negative emotions. This study contributes to a holistic understanding of the socio-economic challenges faced by young adults, advocating for comprehensive support systems that address both the economic and psychological facets of unemployment.

Bridging the Chasm Between Cognitive Representations and Formal Structures of Linguistic Meanings.

This paper aims to show that properties of cognitive/conceptual representations and formal-logical structures of linguistic meaning can be inter-translated, recast, transformed into one another, and so united together, even though cognitive/conceptual representations and formal-logical structures of linguistic meaning are apparently distinct in ontology and divergent in their form or character. While cognitive/conceptual representations are ultimately rooted in sensory-motor systems, formal-logical structures of linguistic meaning are abstractions detached from and independent of the actualized world. This paper sketches out the foundations of how representations of linguistic meaning in terms of cognitive/conceptual structures in Cognitive/Conceptual Semantics can be unified with those in terms of formal-logical structures in Formal Semantics. This is done by recasting cognitive/conceptual representations in terms of formal-logical structures of linguistic meaning and re-encoding formal-logical structures of linguistic meaning in terms of cognitive/conceptual representations. Then, these two types of semantic representations, thus shown representationally equivalent, will be related to a series of derivations across levels in neuronal networks and dynamics. The general discussion on unifying cognitive/conceptual representations of linguistic meaning with formal-logical structures is contextualized within the broader context of theorizing in cognitive science.

ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding.

An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 13 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model. As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language.

Linguistic based emotion analysis using softmax over time attention mechanism.

Recognizing the real emotion of humans is considered the most essential task for any customer feedback or medical applications. There are many methods available to recognize the type of emotion from speech signal by extracting frequency, pitch, and other dominant features. These features are used to train various models to auto-detect various human emotions. We cannot completely rely on the features of speech signals to detect the emotion, for instance, a customer is angry but still, he is speaking at a low voice (frequency components) which will eventually lead to wrong predictions. Even a video-based emotion detection system can be fooled by false facial expressions for various emotions. To rectify this issue, we need to make a parallel model that will train on textual data and make predictions based on the words present in the text. The model will then classify the type of emotions using more comprehensive information, thus making it a more robust model. To address this issue, we have tested four text-based classification models to classify the emotions of a customer. We examined the text-based models and compared their results which showed that the modified Encoder decoder model with attention mechanism trained on textual data achieved an accuracy of 93.5%. This research highlights the pressing need for more robust emotion recognition systems and underscores the potential of transfer models with attention mechanisms to significantly improve feedback management processes and the medical applications.

Effects of mobility on dialect change: Introducing the linguistic mobility index.

Increased geographical mobility prompts dialectologists to factor in survey participants' exposure to linguistic variation in their research. Changing mobility patterns (e.g. longer-distance commuting; easier relocation to distant places for work, study or marriage) have caused linguistic connections to become much more diverse, potentially contributing to the acceleration of dialect change. In this methodological work we propose the Linguistic Mobility Index (LMI) to estimate long-term exposure to dialectal variation and thereby to provide a reference of "localness" about survey participants. Based on data about a survey participant's linguistic biography, an LMI may comprise combinations of influential agents and environments, such as the dialects of parents and long-term partners, the places where participants have lived and worked, and the participants' level of education. We encapsulate the linguistic effects of these agents based on linguistic differences, the intensity and importance of the relationship. We quantify the linguistic effects in three steps. 1) The linguistic effect of an agent is represented by a linguistic distance, 2) This linguistic distance is weighted based on the intensity of the participant's exposure to the agent, and 3) Further weighted according to the relationship embodied by the agent. LMI is conceptualised and evaluated based on 500 speakers from 125 localities in the Swiss German Dialects Across Time and Space (SDATS) corpus, and guidance is provided for establishing LMI in other linguistic studies. For the assessment of LMI's applicability to other studies, four LMI prototypes are constructed based on the SDATS corpus, employing different theoretical considerations and combinations of influential agents and environments to simulate the availability of biographical data in other studies. Using mixed-effects modelling, we evaluate the utility of the LMI prototypes as predictors of dialect change between historic and contemporary linguistic data of Swiss German. The LMI prototypes successfully show that higher exposure to dialectal variation contributes to more dialect change and that its effect is stronger than some sociodemographic variables that are often tested for affecting dialect change (e.g. sex and educational background).

Utility of Kolmogorov complexity measures: Analysis of L2 groups and L1 backgrounds.

The proliferation of automated syntactic complexity tools allowed the analysis of larger amounts of learner writing. However, existing tools tend to be language-specific or depend on segmenting learner production into native-based units of analysis. This study examined the utility of a language-general and unsupervised linguistic complexity metric: Kolmogorov complexity in discriminating between L2 proficiency levels within several languages (Czech, German, Italian, English) and across various L1 backgrounds (N = 10) using two large CEFR-rater learner corpora. Kolmogorov complexity was measured at three levels: syntax, morphology, and overall linguistic complexity. Pairwise comparisons indicated that all Kolmogorov complexity measures discriminated among the proficiency levels within the L2s. L1-based variation in complexity was also observed. Distinct syntactic and morphological complexity patterns were found when L2 English writings were analyzed across versus within L1 backgrounds. These results indicate that Kolmogorov complexity could serve as a valuable metric in L2 writing research due to its cross-linguistic flexibility and holistic nature.

Language-like efficiency and structure in house finch song.

Communication needs to be complex enough to be functional while minimizing learning and production costs. Recent work suggests that the vocalizations and gestures of some songbirds, cetaceans and great apes may conform to linguistic laws that reflect this trade-off between efficiency and complexity. In studies of non-human communication, though, clustering signals into types cannot be done a priori, and decisions about the appropriate grain of analysis may affect statistical signals in the data. The aim of this study was to assess the evidence for language-like efficiency and structure in house finch (Haemorhous mexicanus) song across three levels of granularity in syllable clustering. The results show strong evidence for Zipf's rank-frequency law, Zipf's law of abbreviation and Menzerath's law. Additional analyses show that house finch songs have small-world structure, thought to reflect systematic structure in syntax, and the mutual information decay of sequences is consistent with a combination of Markovian and hierarchical processes. These statistical patterns are robust across three levels of granularity in syllable clustering, pointing to a limited form of scale invariance. In sum, it appears that house finch song has been shaped by pressure for efficiency, possibly to offset the costs of female preferences for complexity.

Risk assessment of deep-sea floating offshore wind power projects based on hesitant fuzzy linguistic term set considering trust relationship among experts.

The development of deep-sea floating offshore wind power (FOWP) is the key to fully utilizing water resources to enhance wind resources in the years ahead, and then the project is still in its initial stage, and identifying risks is a crucial step before promoting a significant undertaking. This paper proposes a framework for identifying risks in deep-sea FOWP projects. First, this paper identifies 16 risk criteria and divides them into 5 groups to establish a criteria system. Second, hesitant fuzzy linguistic term set (HFLTS) and triangular fuzzy number (TFN) are utilized to gather and describe the criterion data to ensure the robustness and completeness of the criterion data. Third, extending the method for removal effects of criteria (MEREC) to the HFLTS environment through the conversion of TFNs, under the influence of subjective preference and objective fairness, a weighting method combining analytic network process (ANP) and MEREC is utilized to calculate criteria weights, and the trust relationship and consistency between experts are used to calculate the expert weights to avoid the subjective weighting given by experts arbitrariness. Fourth, the study's findings indicated that the overall risk level of the deep-sea FOWP projects is "medium." Fifth, sensitivity and comparative analyses were conducted to test the reliability of the assessment outcomes. lastly, this research proposes risk management measures for the deep-sea FOWP project's establishment from economic, policy, technology, environment, and management aspects.