Linguistic Information Research Articles

Towards audiovisual scene analysis and object formation

The neural mechanisms underpinning auditory scene analysis and object formation have been of intense research interest in the past two decades. Fundamentally, however, we live in a multisensory environment. Even Cherry in his original paper posited that “lip reading” as a way for us to solve the cocktail party problem. Yet, how different aspects of visual cues (e.g., timing, linguistic information) help listeners follow conversation in a complex acoustic scene is still not well understood. In this talk, we present a theoretical framework to study audiovisual scene analysis that has been extrapolated from the unisensory object-based attention literature and posit the following questions: How do we define a multi-modal object? What are the predictions from unisensory object-based attention theory when we apply to the audiovisual domain? What are the conceptual models to test the different neural mechanisms that underpin audiovisual scene analysis? Answering these questions would move us closer to addressing the cocktail party problem in the real-world setting as well as help us create, de novo, audiovisual scenes that are more engaging in the augmented/virtual reality world.

Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network KGAN, which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different perspectives, i.e., context-, syntax- and knowledge-based. First, KGAN learns the contextual and syntactic representations in parallel to fully extract the semantic features. Then, KGAN integrates the knowledge graphs into the embedding space, based on which the aspect-specific knowledge representations are further obtained via an attention mechanism. Last, we propose a hierarchical fusion module to complement these multi-view representations in a local-to-global manner. Extensive experiments on five popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN achieves a new record of state-of-the-art performance among all datasets.

Subtitling for people with hearing impairments in the Arab world context: The case of <i>the Blue Elephant 2</i> movie

Although technology advancements have increased the availability of screens everywhere, making it possible to watch AV products anytime, the deaf-and-hard-of-hearing (DHH) accessibility to these materials in the Arab world is still poor. Only a few Arabic movies with this feature are available via Netflix. In this study, the subtitles for the deaf and hard of hearing (SDH) in <i>The Blue Elephant 2</i> movie are analyzed. This study qualitatively examines the type of information covered in the subtitles of the investigated Arabic movie for Arab viewers with hearing impairment. It also quantitatively investigates the reactions of people with hearing impairment to the technical aspects of the subtitles using a structured questionnaire, which consisted of five open-ended questions. The qualitative analysis revealed two main types of information, namely, para-linguistic/extra-linguistic and linguistic. The para-linguistic/extra-linguistic aspect includes information about music, speakers, vocal non-linguistic features, and non-verbal signs. The linguistic aspect is related to the code-switching between languages where the speaker may use some languages other than Arabic. The quantitative analysis showed that the participants reacted positively to the technical aspects of the subtitles, including the length and synchrony. They stated that they had enough time to read and process the information included in the subtitles. The paralinguistic information was reflected in the subtitles and helped them better understand the movie. The sample attitude towards SDH was good and positive, and they like to watch more Arabic movies with this feature in the future. This study concludes that more attention to DHH people in terms of accessibility to audiovisual content should be paid. This will likely improve their linguistic and cultural skills and help them integrate more with the surrounding community. The study’s findings have substantial implications for enhancing DHH individuals’ access to audiovisual content in Jordan and the wider Arab region. The study emphasizes the need to deliver high-quality SDH that incorporates both linguistic and paralinguistic information to DHH viewers in order to improve their viewing experience.

ADVANCING GASIFICATION-COMBINED UP AND DOWN DRAFT GASIFIER-BASED TREATMENT OF TEXTILE WASTE: ASSESSING FEASIBILITY, ENVIRONMENTAL IMPACTS AND ENERGY RECOVERY POTENTIAL

Speech enhancement, a critical component in various applications including voice assistants and telecommunication systems, aims to improve the quality and intelligibility of spoken content. This paper introduces a novel approach leveraging Natural Language Processing (NLP) techniques for speech enhancement. By harnessing the power of NLP models, we propose a methodology that not only considers the acoustic features but also incorporates linguistic context in the enhancement process. The study encompasses data collection, preprocessing, and the application of advanced NLP algorithms to extract relevant linguistic information. The experimental results demonstrate a significant improvement in speech quality, as evidenced by a substantial increase in Signal-to-Noise Ratio (SNR) and Mean Opinion Score (MOS) compared to conventional methods. Furthermore, a comparative analysis with existing techniques showcases the superiority of our approach. This research not only contributes to the advancement of speech enhancement technology but also underscores the pivotal role of NLP in this domain. The findings pave the way for future research directions, suggesting potential applications in areas such as voice recognition systems and real-time communication platforms. Keywords:Acoustic Features, Linguistic Context, Mean Opinion Score (MOS), Natural Language Processing, NLP Algorithms, Signal-to-Noise Ratio (SNR), Speech Enhancement, Speech Quality, Telecommunication Systems, Voice Assistants.

Semantic Transformation of the Content of Gazelles by R. Gatash in their Translation into Russian and Turkish

The current article raises the problem of transferring the artistic features of the gazelles of the Tatar poet R. Gatash (R.K. Gataullin) in translations into Russian and Turkish. The relevance of the study is associated with the growing interest of the modern world in the problems of translation from national languages. The purpose of the work is to identify the features of the transfer of linguistic and cultural information in translations. The gazelles of the Tatar poet R. Gatash from the cycle “Miӊgүzəlgə gazəllər” became the research material. The main research method is a comparative one, aimed at identifying similarities and differences between the works under consideration. As a result of the study, we conclude that the proximity of the two languages and cultures allows the translator into Turkish to fully recreate the features of the poetics of the Tatar poet’s poems in the target language.

Display-Semantic Transformer for Scene Text Recognition.

Linguistic knowledge helps a lot in scene text recognition by providing semantic information to refine the character sequence. The visual model only focuses on the visual texture of characters without actively learning linguistic information, which leads to poor model recognition rates in some noisy (distorted and blurry, etc.) images. In order to address the aforementioned issues, this study builds upon the most recent findings of the Vision Transformer, and our approach (called Display-Semantic Transformer, or DST for short) constructs a masked language model and a semantic visual interaction module. The model can mine deep semantic information from images to assist scene text recognition and improve the robustness of the model. The semantic visual interaction module can better realize the interaction between semantic information and visual features. In this way, the visual features can be enhanced by the semantic information so that the model can achieve a better recognition effect. The experimental results show that our model improves the average recognition accuracy on six benchmark test sets by nearly 2% compared to the baseline. Our model retains the benefits of having a small number of parameters and allows for fast inference speed. Additionally, it attains a more optimal balance between accuracy and speed.

Communication in the Oil and Gas Industry

This article aims to describe some features of communication in the oil and gas industry considering contributions from applied linguistics and Aviation English. Fostered by the need to have a picture of communication dynamics taking place in the proposed scenario, studies were conducted within the HF2 Project and were organized in three stages. Stage 1 revised accident reports to find out communication factors that could be associated with safety and help build a tentative version of a taxonomy based on the Taxonomy of Communication and Language factors in Aviation used for language analysis in aviation. Stage 2 comprehended interviews with workers from two different offshore operations so to assess metalinguistic information regarding a possible standardized language training and use during the performance of activities. Stage 3 encompassed in-loco data collection and analysis of linguistic information. Corpus Linguistics, Conversational Analysis, The Cooperative Principle, and the Taxonomy of Communication and Language factors in Aviation were some of the theoretical references that guided the analysis. The results presented are preliminary, yet significant, and show that procedural factors are outstanding when contemplating the possibility of miscommunication and, because of that, could be considered the core of a taxonomy. Additionally, metalinguistic data from the interviews show that there seems to be a standardized communicative behavior in the operations given the strict technical training to which the workers are submitted to. However, the misuse or non-use of certain lexical-morphological structures and strategies, and procedures could impact safety. In this line, the article also addresses some suggestions for optimized communication practices.

Association rule mining with fuzzy linguistic information based on attribute partial ordered structure

Association rule mining with fuzzy linguistic information based on attribute partial ordered structure

The effects of sensorimotor and linguistic information on the basic-level advantage

The basic-level advantage is one of the best-known effects in human categorisation. Traditional accounts argue that basic-level categories present a maximally informative or entry level into a taxonomic organisation of concepts in semantic memory. However, these explanations are not fully compatible with most recent views on the structure of the conceptual system such as linguistic-simulation accounts, which emphasise the dual role of sensorimotor (i.e., perception-action experience of the world) and linguistic distributional information (i.e., statistical distribution of words in language) in conceptual processing. In four preregistered word→picture categorisation studies, we examined whether novel measures of sensorimotor and linguistic distance contribute to the basic level-advantage in categorical decision-making. Results showed that overlap in sensorimotor experience between category concept and member concept (e.g., animal→dog) predicted RT and accuracy at least as well as a traditional division into discrete subordinate, basic, and superordinate taxonomic levels. Furthermore, linguistic distributional information contributed to capturing effects of graded category structure where typicality ratings did not. Finally, when image label production frequency was taken into account (i.e., how often people actually produced specific labels for images), linguistic distributional information predicted RT and accuracy above and beyond sensorimotor information. These findings add to our understanding of how sensorimotor-linguistic theories of the conceptual system can explain categorisation behaviour.

Utilizing health-related text on social media for depression research: themes and methods

PurposeSocial media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods.Design/methodology/approachThe authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles.Findings(1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife.Practical implicationsThe authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression.Originality/valueThis article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.

Assessing the impact of healthcare service risks on healthcare demand under evolving economic and social structures: An improved GLDS decision making method considering risk attitudes

Faced with backward and uneven economic conditions and an irrational and increasingly inequitable social structure, healthcare disparities are widening and the healthcare inequalities are expanding. This can lead to an increasing public demand for healthcare services, posing unprecedented risk, such as resource shortages, quality deterioration and rising healthcare costs. By identifying, managing, and optimizing the aforementioned risks within the healthcare system, it helps the healthcare system better address the challenges of expanding healthcare demands under evolving economic and social structure. To achieve this, this paper employs failure mode and effects analysis (FMEA) to identify severe failure modes in healthcare services. However, traditional FMEA methods possess limitations in assessing the dynamics of trust relationships and interaction effects among experts within social networks. Therefore, this paper proposes an improved gained and lost dominance score (GLDS) based social network decision-making method considering risk attitudes for FMEA to prioritize failure modes in healthcare services. First, transform linguistic risk assessment information from an interdisciplinary team of experts based on probabilistic linguistic term sets handle uncertain efficiently. Meanwhile, experts’ influence is reflected by an extended PageRank algorithm with trust degrees based on social networks analysis. Next, an improved GLDS method considering the differentiated risk attitudes of individuals and groups is developed to prioritize failure modes. Especially, to solve the problems of soft preference and incomparability relation among failure modes, which are ignored by the traditional GLDS method, a conflict analysis is established to determine the preference, indifference, and incomparability relations. Finally, the practicality of the proposed method is illustrated by the healthcare service risk case in the emergency department and the effectiveness of the developed method is validated by comparison and sensitivity experiments. The results suggest that failure to update labels showing patient severity promptly is the most dangerous. In addition, it would be more economically efficient to analyze the non-comparable failure modes facing different risk factors.

A concept lattice-based expert opinion aggregation method for multi-attribute group decision-making with linguistic information

During the multi-attribute group decision-making (MAGDM) processing, the individuals often hold different opinions about the alternatives. It is necessary to aggregate the different individual opinions into a unified group opinion. In the real world, experts sometimes use linguistic expressions to evaluate attributes in uncertain environments. To address the problem of reducing the information loss of expert opinion aggregation in MAGDM, this paper proposes a MAGDM approach based on linguistic concept lattices in the context of uncertain linguistic expression. A linguistic concept lattice for multi-expert linguistic formal context is first constructed based on linguistic truth-valued lattice implication algebra, which can express both comparable and incomparable linguistic information in the decision-making process. Different expert opinions are aggregated via the extent of fuzzy linguistic concepts, which can reduce information loss in the aggregation process. Second, meet-irreducible elements in the linguistic concept lattice are introduced to reduce the computational complexity of obtaining all fuzzy linguistic concepts in the decision-making process. the distance between the intents of different fuzzy linguistic concepts is considered to enhance the rationality of linguistic decision results. In addition, the expert’s decision-making process for each alternative is visualized via linguistic concept lattices. Finally, the case study and comparative analysis illustrate the validity and rationality of the proposed approach in MAGDM with linguistic information.

Dawn of the Transformer Era in Speech Emotion Recognition: Closing the Valence Gap.

Recent advances in transformer-based architectures have shown promise in several machine learning tasks. In the audio domain, such architectures have been successfully utilised in the field of speech emotion recognition (SER). However, existing works have not evaluated the influence of model size and pre-training data on downstream performance, and have shown limited attention to generalisation, robustness, fairness, and efficiency. The present contribution conducts a thorough analysis of these aspects on several pre-trained variants of wav2vec2.0 and HuBERT that we fine-tuned on the dimensions arousal, dominance, and valence of MSP-Podcast, while additionally using IEMOCAP and MOSI to test cross-corpus generalisation. To the best of our knowledge, we obtain the top performance for valence prediction without use of explicit linguistic information, with a concordance correlation coefficient (CCC) of. 638 on MSP-Podcast. Our investigations reveal that transformer-based architectures are more robust compared to a CNN-based baseline and fair with respect to gender groups, but not towards individual speakers. Finally, we show that their success on valence is based on implicit linguistic information, which explains why they perform on-par with recent multimodal approaches that explicitly utilise textual information. To make our findings reproducible, we release the best performing model to the community.

The Development of Predictive Gender Processing Strategies During Noun Phrase Decoding: An Eye-Tracking Study With German 5- to 10-Year-Old Children.

Many models of language comprehension assume that listeners predict the continuation of an incoming linguistic stimulus immediately after its onset, based on only partial linguistic and contextual information. Their related developmental models try to determine which cues (e.g., semantic or morphosyntactic) trigger such prediction, and to which extent, during different periods of language acquisition. One morphosyntactic cue utilized predictively in many languages, inter alia German, is grammatical gender. However, studies of the developmental trajectories of the acquisition of predictive gender processing in German remain a few. This study attempts to shed light on such processing strategies used in noun phrase decoding among children acquiring German as their first language by examining their eye movements during a language-picture matching task (N = 78, 5-10 years old). Its aim was to confirm whether the eye movements indicated the presence of age-specific differences in the processing of a gender cue, provided either in isolation or in combination with a semantic cue. The results revealed that German children made use of predictive gender processing strategies from the age of 5 years onward; however, the pace of online gender processing, as well as confidence in the predicted continuation, increased up to the age of 10 years. Predictive processing of gender cues plays a role in German language comprehension even in children younger than 8 years.

Entity generation algorithm based on reference expansion

The extraction and understanding of text knowledge become increasingly crucial in the age of big data. One of the current research areas in the field of natural language processing (NLP) is how to accurately understand the text and collect accurate linguistic information because Chinese vocabulary is diverse and ambiguous. This paper mainly studies the candidate entity generation module of the entity link system. The candidate entity generation module constructs an entity reference expansion algorithm to improve the recall rate of candidate entities. In order to improve the efficiency of the connection algorithm of the entire system while ensuring the recall rate of candidate entities, we design a graph model filtering algorithm that fuses shallow semantic information to filter the list of candidate entities, and verify and analyze the efficiency of the algorithm through experiments. By analyzing the related technology of the entity linking algorithm, we study the related technology of candidate entity generation and entity disambiguation, improve the traditional entity linking algorithm, and give an innovative and practical entity linking model. The recall rate exceeds 82%, and the link accuracy rate exceeds 73%. Efficient and accurate entity linking can help machines to better understand text semantics, further promoting the development of NLP and improving the users’ knowledge acquisition experience on the text.

Retracted: A Statistical English Syntax Analysis Model Based on Linguistic Evaluation Information

Retracted: A Statistical English Syntax Analysis Model Based on Linguistic Evaluation Information

DSG-GAN: Multi-turn text-to-image synthesis via dual semantic-stream guidance with global and local linguistics

Multi-turn text-to-image synthesis task aims to manipulate desired visual content according to the user's intention step by step, which has recently attracted a lot of research interest in the community of language and vision. Different from traditional text-to-image synthesis, multi-turn text-to-image synthesis is more challenging as 1) it needs to continuously recognize the user's intention from spoken instruction and perceive the visual information from the source image; 2) it requires reasoning about the position, appearance, and characteristics of fresh modifications in target images as well as connecting objects in instructions with visual components in source images. To deal with this issue, in this paper, we propose a Dual Semantic-stream Guidance with global and local linguistics Generative Adversarial Network (DSG-GAN), which reasons and learns the user's intention from text description and iteratively manipulates visual information. Specifically, we design a novel dual semantic-stream discriminator, which combines with a hierarchical instruction encoder to evaluate the logic and quality between human intention in linguistic instruction and generates visual content from the perspective of global and fine-grained consistency matching. Meanwhile, the discriminator's backpropagation gradient is used to optimize the instruction encoder, which incentivizes it to purify the user's intention into global and local information that is consistent with the manipulation's visual representation. Extensive experiments show that even when producing high-resolution images and making deep iterative turns, our method performs significantly better due to local fine-grained linguistic information being combined with cross-modal correlation.

Average consistency index based consensus model for a group decision making problem dealing with ELICIT expressions

A group decision making (GDM) problem is often modeled by using a reciprocal preference relation (RPR) structure. Linguistic information is widely used in RPRs to depict its components because it facilitates its understanding and management of uncertainty. Among the existing linguistic representation models, the extended comparative linguistic expressions with symbolic translation (ELICIT) model improves the accuracy of computational processes and maintains the comprehensibility and interpretability of the results. Therefore, we introduce a new linguistic RPR (L-RPR) structure using ELICIT information, denoted as ELICIT-RPR, whose elements are uniformed as 2-tuple linguistic intervals to simplify and facilitate computations between ELICIT expressions. In addition, the average consistency index (ACI) of ELICIT-RPR is proposed together an algorithm for improving the ACI values of ELICIT-RPRs based on their best and worst consistency indices. To avoid conflicts and obtain agreed solutions, consensus reaching processes (CRPs) have been widely used in GDM. Thus, a novel CRP based on ACI values is developed using a new distance between linguistic intervals. Finally, a case study and a comparison analysis are presented to demonstrate the validity and applicability of the proposal.

EFFECTS OF LEARNER-CENTERED INTERVENTIONS IN SCIENCE LEARNING: COMPARING EYE MOVEMENT IN EYE MOVEMENT MODELING EXAMPLES AND PROMPTING

An integrated understanding of illustrations and texts is crucial for students to comprehend science teaching learning materials. Intervention methods can be employed to facilitate this integrated understanding. This study examined the effects of Eye Movement Modeling Examples (EMME) and Prompting interventions on the learning of illustrated scientific texts among primary school students. Furthermore, the study compared the reading strategies and learning comprehension of fifth-grade students who utilized EMME, an approach that facilitates learning through observational methods, with those who used Prompting, a method that directly guides learning through linguistic information. The study analysis revealed that EMME had a significantly greater effect on integrated reading strategies and learning comprehension compared with Prompting. Specifically, the EMME group exhibited an integrated reading strategy that involved relating the illustrations and texts, whereas the Prompting group and the control group did not demonstrate the same strategy. In addition, the post-test results indicated that the EMME group achieved higher average scores in verbal and pictorial factors compared with the other two groups. Keywords: eye movement modeling examples, learner-oriented intervention, science education, science learning, South Korea

New approach for quality function deployment based on social network analysis and interval 2-tuple Pythagorean fuzzy linguistic information

Quality function deployment (QFD) is a useful quality management tool for the transformation of customer requirements (CRs) into engineering characteristics (ECs) in product development. In the literature, a lot of QFD approaches have been proposed to overcome the deficiencies and enhance the performance of the traditional QFD method. However, there are several issues deserving further investigation for improving QFD: majority of the existing methods describe experts’ opinions with single linguistic terms directly, which may produce the loss of information; most of the improved QFD methods cannot take the consensus levels among experts into account. In this study, we develop a new integrated QFD approach based on the interval 2-tuple Pythagorean fuzzy linguistic sets (I2PFLSs), the social network consensus reaching (SNCR) model, and an extended combined compromise solution (CoCoSo) method. There are three main contributions of the presented study: First, the correlation assessments between CRs and ECs given by experts are handled with the I2PFLSs. Second, the SNCR with minimum adjustment distance is employed to coordinate experts’ conflict correlation assessments by considering their trust relationships. Third, an extended CoCoSo method is employed to determine the importance ranking of ECs within the interval 2-tuple Pythagorean fuzzy linguistic environment. Finally, we present an electric vehicle product development case to demonstrate the effectiveness and superiority of the proposed QFD approach. The results show that the new QFD can express experts’ fuzzy and uncertain linguistic assessment information flexibly, deal with experts’ consensus in correlation assessment process, and yield more precise importance ranking of ECs for product improvement.