Semantic Domain Research Articles

Words for the hearts: a corpus study of metaphors in online depression communities.

Humans understand, think, and express themselves through metaphors. The current paper emphasizes the importance of identifying the metaphorical language used in online health communities (OHC) to understand how users frame and make sense of their experiences, which can boost the effectiveness of counseling and interventions for this population. We used a web crawler to obtain a corpus of an online depression community. We introduced a three-stage procedure for metaphor identification in a Chinese Corpus: (1) combine MIPVU to identify metaphorical expressions (ME) bottom-up and formulate preliminary working hypotheses; (2) collect more ME top-down in the corpus by performing semantic domain analysis on identified ME; and (3) analyze ME and categorize conceptual metaphors using a reference list. In this way, we have gained a greater understanding of how depression sufferers conceptualize their experience metaphorically in an under-represented language in the literature (Chinese) of a new genre (online health community). Main conceptual metaphors for depression are classified into PERSONAL LIFE, INTERPERSONAL RELATIONSHIP, TIME, and CYBERCULTURE metaphors. Identifying depression metaphors in the Chinese corpus pinpoints the sociocultural environment people with depression are experiencing: lack of offline support, social stigmatization, and substitutability of offline support with online support. We confirm a number of depression metaphors found in other languages, providing a theoretical basis for researching, identifying, and treating depression in multilingual settings. Our study also identifies new metaphors with source-target connections based on embodied, sociocultural, and idiosyncratic levels. From these three levels, we analyze metaphor research's theoretical and practical implications, finding ways to emphasize its inherent cross-disciplinarity meaningfully.

Convexity and Monotonicity in Language Coordination: Simulating the Emergence of Semantic Universals in Populations of Cognitive Agents

Natural languages vary in their quantity expressions, but the variation seems to be constrained by general properties, so-called universals. Their explanations have been sought among constraints of human cognition, communication, complexity, and pragmatics. In this article, we apply a state-of-the-art language coordination model to the semantic domain of quantities to examine whether two quantity universals—monotonicity and convexity—arise as a result of coordination. Assuming precise number perception by the agents, we evolve communicatively usable quantity terminologies in two separate conditions: a numeric-based condition in which agents communicate about a number of objects and a quotient-based condition in which agents communicate about the proportions. We find out that both universals take off in all conditions but only convexity almost entirely dominates the emergent languages. Additionally, we examine whether the perceptual constraints of the agents can contribute to the further development of universals. We compare the degrees of convexity and monotonicity of languages evolving in populations of agents with precise and approximate number sense. The results suggest that approximate number sense significantly reinforces monotonicity and leads to further enhancement of convexity. Last but not least, we show that the properties of the evolved quantifiers match certain invariance properties from generalized quantifier theory.

A Novel Mapper Machine Learning Algorithm for Semantic Domain Mapping for Domain Database Updation

A Novel Mapper Machine Learning Algorithm for Semantic Domain Mapping for Domain Database Updation

Semantic domain comparison of research keywords by indicator-based fuzzy distances: A new prospect

Assessing the similarity of scientific outputs based on an indicator has not been addressed much so far. The topic, however, may find several potential applications which can help enrich procedures of ranking, research monitoring, and scientific policy-making. The present study offers a new method to quantify such similarities based on keyword co-occurrence matrices. In the proposed method, first, the keyword co-occurrence networks are transformed into their associated newly defined fuzzy sets, named as scientosemantic domains. Then, a fuzzy distance between the two domains is found based on an arbitrary indicator. In this paper, the three indicators of frequency, development and investment appeal are used. The proposed method is implemented for five types of concept comparison. For each type, concepts are represented by a canonical keyword with different field codes. Scientosemantic domains of concepts are sourced out of bibliometric data obtained from appropriate queries on SCOPUS. Number of keywords used to define scientosemantic domains ranges from about 30 to 800. Since indicator-based comparison of scientosemantic domains are not dealt with in the literature, the obtained distances between concepts are verified by qualitative and expert evaluations. For all cases, frequency- and development-based distances are less than those for investment appeal; while crisp distances for the latter extend beyond 0.6, the former does not exceed 0.3. The greatest distances are observed for investment appeal in technology-related keywords.

Natural Semantic Metalanguage Analysis of English Speech Act Verb ‘Inform’

Generally, every language has a lexicon that is grouped into classified verbs. Each verb can be broken down into some specific subtypes. Particularly in English, verbs are semantically divided into several subtypes. Verbs such as tell, ask, inform, offer, forgive, and others are belonging to the types of verbs of speech or speech acts. This study aims to analyze natural semantic metalanguage analysis of English speech act verb of inform. In collecting data is using observational method. Therefore, the theory of natural semantic metalanguage (NSM) is used to analyze the data taken from the American Corpus (COCA). By way of explication, the particular lexicon is explained thoroughly to reveal the subtle meaning of semantic features without any bias. The research argues that the four subtypes of the speech act verb "inform" demonstrate varying semantic forms and structures. The distinctions in lexicon become evident within their common semantic domain. Moreover, the four associated lexicons with the verb "inform" possess unique semantic traits and serve specific purposes within speech acts.

Representing the ‘Little Fresh Meat’ Phenomenon in the Chinese English-Medium News Media: A Corpus-Assisted Critical Discourse Analysis

In China, the phenomenon of ‘little fresh meat’ has recently sparked a contentious debate. However, few studies have examined the thematic concepts surrounding the phenomenon of ‘little fresh meat’ in the Chinese English-medium news media. In order to fill this gap, this study used corpus-assisted critical discourse analysis to examine how the ‘little fresh meat’ phenomenon has been represented discursively in Chinese English-language news media and observe which stance the media take on this phenomenon. The findings show that four semantic domains, namely, entertainment, education, personal care, as well as gender and sexuality, are inextricably intertwined with the ‘little fresh meat’ phenomenon. The findings also show that patriarchal discourse is prominent, and resistance discourse is only subordinate in the representations. This paper sheds light on debates surrounding the ‘little fresh meat’ phenomenon and demonstrates state gender governance through China’s state media.

Lexical Borrowings from Spanish into Wayuunaiki: Contact, Classification, and Motivations

This study identifies and analyzes lexical borrowings from Spanish into Wayuunaiki (an Arawak language spoken in Colombia and Venezuela). The analysis, based on bibliographic documentation and fieldwork, focuses on the borrowability and semantic domains of Spanish loanwords and the factors motivating Spanish loanword adoption into Wayuunaiki. The results show that, despite the typological distance, Wayuunaiki is prone to adopting lexical items from Spanish, as evident in the moderate number of lexical borrowings identified. A key motivating factor for Spanish loanword incorporation is Wayuunaiki speakers’ need for lexical items to refer to new concepts adopted from the dominant Spanish-speaking culture. This phenomenon is partly due to the contact dynamics between Wayuunaiki and Spanish. Besides expanding on the description of the Wayuunaiki language and its contact with Spanish, this study contributes to identifying factors and motivations favoring the adoption of loanwords between typologically distant languages.

Rensets and Renaming-Based Recursion for Syntax with Bindings Extended Version

We introduce renaming-enriched sets (rensets for short), which are algebraic structures axiomatizing fundamental properties of renaming (also known as variable-for-variable substitution) on syntax with bindings. Rensets compare favorably in some respects with the well-known foundation based on nominal sets. In particular, renaming is a more fundamental operator than the nominal swapping operator and enjoys a simpler, equationally expressed relationship with the variable-freshness predicate. Together with some natural axioms matching properties of the syntactic constructors, rensets yield a truly minimalistic characterization of lambda -calculus terms as an abstract datatype—one involving an infinite set of unconditional equations, referring only to the most fundamental term operators: the constructors and renaming. This characterization yields a recursion principle, which (similarly to the case of nominal sets) can be improved by incorporating Barendregt’s variable convention. When interpreting syntax in semantic domains, our renaming-based recursor is easier to deploy than the nominal recursor. Our results have been validated with the proof assistant Isabelle/HOL.

Where didwergo? Lexical variation and change in third-person male adult noun referents in Old and Middle English

AbstractThe present study uses variationist quantitative methods to examine the evolution of the semantic field of third-person male adult noun referents from Old English to Middle English, covering a time depth of approximately six hundred years. Results show a shift from the favored variantwerin Old English tomanin Middle English, with the diachronic change in frequency following a prototypical s-shaped distribution. Although the replacement seems to take centuries to be complete, lexical frequency and written transmission are proposed as influential explanatory factors, and a homonymic clash is suggested to have accelerated the process of replacement in Middle English. Text type and text origin contribute to variation, with alliteration significantly influencing lexical choices in Old English verse texts. When combined with findings from recent synchronic work, this study highlights a heterogeneously structured semantic domain, which has undergone lexical replacement and change over time, providing some evidence for the applicability of s-shaped patterns for lexical change.

Built Environment Cybersecurity: Development and Validation of a Semantically Defined Access Management Framework on a University Case Study

To achieve the potential of smart cities, there is a strong requirement to use a set of useful, but still accessible services within smart city systems. Interoperability challenges and roadblocks for software developers and integrators are well-known consequences of fragmented semantics across different contexts. Furthermore, in the smart city context, there is a need to ensure the security and identity of real-world services operating on this data through the adoption of access control principles (authorization and authentication). The use of ontologies to unify the diverse semantics of multiple domains is one strategy that has had considerable success in the past. This paper describes an access management ontology in smart cities developed to enable the interoperability of physical built environment assets, sensing and actuation devices and current built environment services with existing security standards, digital twin and Building Information Model (BIM) datasets. It also provides interoperability between user interfaces and the actors who use them in the context of establishing an access management in smart cities framework for the built environment. This has been validated through its implementation in the Cardiff Urban Sustainability Platform (CUSP), deployed to manage a variety of smart services on a university campus. This validation has successfully shown the ability of the ontology to function as intended in the context of a digital twin, thereby offering single sign-on and suitable access control.

Embodiment of color metaphor: an image-based visual analysis of the Chinese color terms hēi ‘black’ and bái ‘white’

AbstractPerceptual information includes sensorimotor and emotional experience regarding the multimodality of the perceptual system. The current study provides an image-based visual analysis on the embodiment of color metaphors through the investigation of (i) the perceptual (dis)similarities between the literal and metaphorical meanings of the Chinese color terms hēi ‘black’ and bái ‘white’ and (ii) the influence of emotional valence on the degree of their perceptual (dis)similarities. Specifically, 24 concepts in three semantic domains were represented as eight-dimensional vectors based on the color information extracted from online images, including two color concepts of black and white, 20 abstract concepts referring to 8 metaphorical meanings of hēi and 12 metaphorical meanings of bái, and two abstract concepts referring to positive and negative affective polarity. Statistical analyses show that (i) the literal and metaphorical meanings of hēi and bái are perceptually distinguishable given their significant perceptual (dis)similarities and (ii) the observed perceptual distinguishability cannot be solely attributed to the (in)consistency of emotional valence associated with the senses. The present study provides nonlinguistic evidence for the embodiment of color metaphors in the Chinese context with an empirical approach that can simultaneously capture the metaphorical mappings and affective associations among cross-domain concepts with sensory data.

A Novel Encoder-Decoder Structure-based Transformer for Fine-Resolution Remote Sensing Images

Full convolution neural network (FCN) based on an encoder-decoder structure has become a standard network in the semantic segmentation domain. Encoder-decoder architecture is an effective means to get finer-grained performance. Encoders constantly extract multilevel features, and then use decoders to gradually introduce low-level features into high-level features. Context information is critical for accurate segmentation, which is the main direction of semantic segmentation at present. So many efforts have been made to make better use of this kind of information, including codec structure, void convolution (expanded convolution), and attention mechanism. However, most of these schemes are based on Resnet or other variants of convolution network FCN, which makes it unable to get rid of the defective local receptive field of convolution itself. In this work, we introduce the pyramid visual converter (PVT) to replace the traditional full convolution network architecture, and design a novel encoder-decoder architecture to more effectively utilize the context information.

Model division multiple access for semantic communications

In a multi-user system, system resources should be allocated to different users. In traditional communication systems, system resources generally include time, frequency, space, and power, so multiple access technologies such as time division multiple access (TDMA), frequency division multiple access (FDMA), space division multiple access (SDMA), code division multiple access (CDMA), and non-orthogonal multiple access (NOMA) are widely used. In semantic communication, which is considered a new paradigm of the next-generation communication system, we extract high-dimensional features from signal sources in a model-based artificial intelligence approach from a semantic perspective and construct a model information space for signal sources and channel features. From the high-dimensional semantic space, we excavate the shared and personalized information of semantic information and propose a novel multiple access technology, named model division multiple access (MDMA), which is based on the resource of the semantic domain. From the perspective of information theory, we prove that MDMA can attain more performance gains than traditional multiple access technologies. Simulation results show that MDMA saves more bandwidth resources than traditional multiple access technologies, and that MDMA has at least a 5-dB advantage over NOMA in the additive white Gaussian noise (AWGN) channel under the low signal-to-noise (SNR) condition.

THE EARLY 20 CENTURY ENGLISH BORROWABILITY FROM THE ROMANCE LANGUAGES

The article focuses on a lexicographic investigation of foreign words with special focus on the borrowings from the Romance languages other than French in the English language in the early 20 century (E20). Language distribution of the research proved that the Romance languages donated significant vocabulary into English in E20. Historical background was studied for better understanding of the respective cultural standards of the analyzed languages and the effect of their lexical influence. The article proposes the qualitative and quantitative interpretation of Latin, Italian, Spanish and Portuguese words: in the language distribution 59,4% % of foreign words were borrowed from these languages. It was proved that words from different semantic domains differ in their borrowability: Latin as the important supplier of vocabulary into English throughout its history (medicine 17,8%, literature 14,2%, law terminology 14,2%, educational terms 7,1%, theology 3,5% etc.); Italian (food and drink 28,6%, art, painting literature terms 18,5%, musical vocabulary 14,8%); Spanish (cultural peculiarities 40%, food items 20%), Portuguese (dance names, clothes items). Differentiation into word classes of borrowed words (other than French) was given and it has been found out that nouns are more borrowable than other parts of speech in all described languages. Morphological characteristics of Italian noun-forming suffixes was given: 59% root nouns, nouns with the most common diminutive suffix -ino/ine/ini – 13,6%, popular diminutive suffix -etto/etta, often used with affectionate overtones – 9%, -ella – 9%, nouns with augmentatives -one/ona – 4,5%, etc. The author hopes that the presented classification of borrowed Italic vocabulary of E20 performed on the basis of lexicographic investigation is undoubtedly a promising field of research. The results of this study are of direct practical relevance.

A domain semantics-enhanced relation extraction model for identifying the railway safety risk

The identification of railway safety risk is important in ensuring continuous and stable railway operations. Most works fail to consider the important relation between detected objects. In addition, poor domain semantics directly degrades the final performance due to difficulty in understanding railway text. To solve these challenging issues, we introduce the triple knowledge from knowledge graph to model the railway safety risk with the knowledge interconnection mode. Afterward, we recast the identification of railway safety risk as the relation extraction task, and propose a novel and effective Domain Semantics-Enhanced Relation Extraction (DSERE) model. Specifically, we design a domain semantics-enhanced transformer mechanism that automatically enhances the railway semantics from a dedicated railway lexicon. We further introduce piece-wise convolution neural networks to explore the fine-grained features contained in the structure of triple knowledge. With the domain semantics and fine-grained features, our model can fully understand the domain text and thus improve the performance of relation classification. Finally, the DSERE model is used to identify the railway safety risk of south zone of China Railway, and achieves 81.84% AUC and 76.00% F1 scores on the real-world dataset showing the superiority of our proposed model.

Neuroendocrine response to a psychosocial stress test is not related to schizotypy but cortisol elevation predicts inflexibility of semantic memory retrieval

An altered stress response can contribute to the transition from preclinical psychotic symptoms to the clinical manifestation of schizophrenia and other psychotic disorders. The present study was aimed at testing the hypotheses that (i) the autonomic and neuroendocrine responses under psychosocial stress are dysregulated in individuals with high psychosis proneness (schizotypy); (ii) the magnitude of post-stress autonomic activation and cortisol release predicts alterations in semantic memory retrieval. The study was performed in 73 healthy individuals of both sexes with either high or low schizotypal traits preselected out of 609 individuals using the Schizotypal Personality Questionnaire. A psychosocial stress procedure based on public speech was used as a stress model. We found that individuals with high schizotypy engaged in less adaptive emotional stress-coping strategies than low schizotypy individuals. Yet, the neuroendocrine, immune, and sympathetic activation in response to the stress test was not different between the groups. Irrespective of the exposure to the stressor, individuals with high schizotypy were less fluent when retrieving associations from semantic memory. In addition, we demonstrated that acute psychosocial stress reduced the flexibility of semantic memory retrieval. The post-stress mental inflexibility was reliably predicted by the concomitant elevation of cortisol concentrations in saliva. The present study thus brings novel evidence indicating that the acute psychosocial challenge impairs retrieval flexibility in the semantic domain, which may be due to neuroendocrine activation.

Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema

Task-oriented dialog systems empower users to accom-plish their goals by facilitating intuitive and expres-sive natural language interactions. State-of-the-art ap-proaches in task-oriented dialog systems formulate theproblem as a conditional sequence generation task andfine-tune pre-trained causal language models in the su-pervised setting. This requires labeled training datafor each new domain or task, and acquiring such datais prohibitively laborious and expensive, thus makingit a bottleneck for scaling systems to a wide rangeof domains. To overcome this challenge, we intro-duce a novel Zero-Shot generalizable end-to-end Task-oriented Dialog system, ZS-ToD, that leverages domainschemas to allow for robust generalization to unseen do-mains and exploits effective summarization of the dia-log history. We employ GPT-2 as a backbone model andintroduce a two-step training process where the goal ofthe first step is to learn the general structure of the dialogdata and the second step optimizes the response gen-eration as well as intermediate outputs, such as dialogstate and system actions. As opposed to state-of-the-artsystems that are trained to fulfill certain intents in thegiven domains and memorize task-specific conversa-tional patterns, ZS-ToD learns generic task-completionskills by comprehending domain semantics via domainschemas and generalizing to unseen domains seam-lessly. We conduct an extensive experimental evaluationon SGD and SGD-X datasets that span up to 20 uniquedomains and ZS-ToD outperforms state-of-the-art sys-tems on key metrics, with an improvement of +17% onjoint goal accuracy and +5 on inform. Additionally,we present a detailed ablation study to demonstrate theeffectiveness of the proposed components and trainingmechanism.

TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency

Accurate segmentation of brain tumors from medical images is important for diagnosis and treatment planning, and it often requires multi-modal or contrast-enhanced images. However, in practice some modalities of a patient may be absent. Synthesizing the missing modality has a potential for filling this gap and achieving high segmentation performance. Existing methods often treat the synthesis and segmentation tasks separately or consider them jointly but without effective regularization of the complex joint model, leading to limited performance. We propose a novel brain Tumor Image Synthesis and Segmentation network (TISS-Net) that obtains the synthesized target modality and segmentation of brain tumors end-to-end with high performance. First, we propose a dual-task-regularized generator that simultaneously obtains a synthesized target modality and a coarse segmentation, which leverages a tumor-aware synthesis loss with perceptibility regularization to minimize the high-level semantic domain gap between synthesized and real target modalities. Based on the synthesized image and the coarse segmentation, we further propose a dual-task segmentor that predicts a refined segmentation and error in the coarse segmentation simultaneously, where a consistency between these two predictions is introduced for regularization. Our TISS-Net was validated with two applications: synthesizing FLAIR images for whole glioma segmentation, and synthesizing contrast-enhanced T1 images for Vestibular Schwannoma segmentation. Experimental results showed that our TISS-Net largely improved the segmentation accuracy compared with direct segmentation from the available modalities, and it outperformed state-of-the-art image synthesis-based segmentation methods.

TV series as disseminators of emerging vocabulary: Non-codified expressions in the TV Corpus

Abstract This study presents a method for identifying words that appear in corpus data earlier than their first date of attestation in dictionaries. We demonstrate the application of this method based on a large diachronic corpus, the TV Corpus, and the Oxford English Dictionary (OED). Combining automatic extraction of candidate terms from the TV Corpus with comprehensive manual analysis and verification, the method identifies 32 words that were used in TV series before their first attestation in the OED. We present a detailed discussion of these words, analysing their distribution across decades and genres of the TV Corpus, their origins, semantic domains and word-formation processes. We also present extracts with their first uses in the TV Corpus and analyse how the words were presented to the large and anonymous mass audience. Our study shows that the method we present is suitable for identifying early attestations of words in large corpora, even though in the case of the TV Corpus, a great deal of manual analysis and verification is needed. In addition, we argue that TV series and other types of fictional texts are an important resource for studying the coinage and spread of terms, due to their function and the fact that they address a mass audience.

The Evolution of Semantic Domain that Both Noun and Verb’s Distinction from the Perspective of the Ancient Chinese Character “Du (度)”

The Evolution of Semantic Domain that Both Noun and Verb’s Distinction from the Perspective of the Ancient Chinese Character “Du (度)”