Semantic Scale Research Articles

JST and rJST: joint estimation of sentiment and topics in textual data using a semi-supervised approach

ABSTRACT This paper demonstrates the performance of the Joint Sentiment Topic model (JST) and the reversed Joint Sentiment Topic model (rJST) in measuring sentiment in political speeches, comparing them against a set of popular methods for sentiment analysis: widely used off-the-shelf sentiment dictionaries; an embeddings-enhanced dictionary approach; Latent Semantic Scaling, a semi-supervised approach; and a zero-shot transformer-based approach using a large language model (GPT-4). The findings reveal JST’s superiority over all non-transformer-based approaches in predicting human-coded sentiment in multiple languages and its ability to replicate known sentiment trends in legislative speech. rJST, meanwhile, provides valuable topic-specific sentiment estimates, responsive to political dynamics and significant events. Both models are, however, outperformed by transformer-based models like GPT-4. Additionally, the paper introduces the ’sentitopics’ R-package, designed to facilitate the use of JST and rJST in computational text analysis workflows. This package is compatible with popular text analysis tools, making the models accessible for applied researchers in communication science.

Development and validation of the 5-Dimension Comprehensive Assessment Scale (5DCAS) for assessing physical function and health in axial spondyloarthritis.

To describe the development and validation of a novel patient reported scale, which is a comprehensive assessment of the physical function and health specific for patients with axial spondyloarthritis (axSpA). This is a multiphase, mixed methods study. Based on opinion collection and discussions of multidisciplinary consensus meetings and patients, an initial item pool covering all of the ranges of functioning was generated. The item optimization, model fit, response category functioning, differential item functioning, reliability, structure validity, and unidimensionality were tested by confirmatory factor analysis and Rasch measurement theory framework. After the consensus meeting and the two rounds of surveys in patients with axSpA, the initial pool of 135 items was reduced to 25 items formed in five dimensions, which exhibited preferable item reliability, item fit, and person fit to the Rasch model. The Five-Dimensional Comprehensive Assessment Scale (5DCAS) had the best reliability and validity (Kaiser-Meyer-Olkin was 0.919, and the standardized Cronbach's α coefficient was 0.932). The final version of 5DCAS had good unidimensionality, and the Person Separation Index ranged from 0.77 to 0.85. 5DCAS significantly correlated with ASAS-HI, SF-36, BASFI, and disease activity with p values of < 0.001. 5DCAS is a novel patient-reported outcome specific to axSpA, and it forms five dimensions providing a linear sum score of 25 items. 5DCAS comprehensively and significantly represents the physical function and health status of patients with axSpA, although its performance needs further validation in future clinical practices. Key Points • The primary goal in the management of axial spondyloarthritis is to maximize health-related quality of life. Except for the current instruments of ASAS-HI, BASFI, or SF-36, the heterogeneous clinical symptoms and rapid updated treat-to-target concept require a new instrument which can comprehensive and significant evaluate the changes of physical function and health-related quality of life due to disease. • 5DCAS is a novel patient-reported outcome specific to axSpA, and it forms five dimensions providing a linear sum score of 25 items, which contained aspect of pain involvement, spine mobility, global body performance and activity, social participation and environment, and mental health. All of the items were set to a 4-point semantic rating scale measuring severity, frequency, or interference from score 0 to 3. Total 5DCAS score ranges from 0 to 75; higher scores represented greater symptom burden and worse physical function. • 5DCAS is a comprehensive, multidisciplinary, and convenient disease outcome measurement specific for axSpA. It provides a new evaluation instrument in clinical trial and treat-to-target clinical remission for patients and physicians, and also provides a sensitive and accurate assessment standard for optimized health benefits.

Zooming in and out of semantics: proximal-distal construal levels and prominence hierarchies.

We argue that the "Prominence Hierarchy" within linguistics can be subsumed under the "Construal Level Theory" within psychology and that a wide spectrum of grammatical phenomena, ranging from case assignment to number, definiteness, verbal agreement, voice, direct/inverse morphology, and syntactic word-order respond to Prominence Hierarchies (PH), or semantic scales. In fact, the field of prominence hierarchies, as expressed through the languages of the world, continues to be riddled with riddles. We identify a set of conundrums: (A) vantage point and animacy, (B) individuation and narrow reference phenomena, (C) fronting mechanisms, (D) abstraction, and (E) cultural variance and flexibility. We here propose an account for the existence of these hierarchies and their pervasive effects on grammar by relying on psychological Construal Level Theory (CLT). We suggest that both PH and CLT structure the external world according to proximity or distance from the "Me, Here and Now" (MHN) perspective. In language, MHN has the effect of structuring grammars; in cognition, it structures our lives, our preferences, and choices.

The effects of icon design features on user perception and preference: A case study of icons for Covid-19.

The objective of this study is to evaluate users' perceptions and preferences on the design features of the COVID-19 prevention promotion icon from the perspective of users' aesthetic and perceptual needs. In this study, 120 officially published icons from 24 countries and regions were collected from online platforms for ranking tests, and then the top-ranked icons were subjectively rated by the semantic differential method. By evaluating the quality of users' perceptions of multiple semantic dimensions of icons, we extracted the perceptual semantic words that users valued as the main icon design features. Spearmen correlations were applied to derive possible correlations between user rankings and semantic scales, and a Friedman test was also conducted to determine the true differences in user perceptions and preferences for different styles of icons. Factor analysis was conducted to extract six perceptual words that influence the design features of the COVID-19 prevention promotion icon. The methodology adopted in this study facilitated the screening of design features related to icon effectiveness, and the findings show that "Interesting," "Simple," "Familiar, "Recognizable," "Concrete," and "Close(semantic distance)" are the key features that influence users' perception and preference of COVID-19 icon design. The results of this study can be used as the basis for designing and improving publicity icons for preventive measures in COVID-19, and the methods adopted in this study can be applied to evaluate other types of icon design.

On Ordinal Information-Based Weighting Methods and Comparison Analyses

In this paper, we focus on weighting methods within multi-attribute utility/value theory (MAUT/MAVT). In these methods, the decision maker (DM) provides ordinal information about the relative importance of criteria, but also additional information concerning the strength of the differences between the ranked criteria, which can be expressed in different forms, including precise/imprecise cardinal information, ratio-based methods, a ranking of differences, a semantic scale, or preference statements. Although many comparison analyses of weighting methods based on ordinal information have been carried out in the literature, these analyses do not cover all of the available methods, and it is not possible to identify the best one depending on the information provided by the DM. We review the analyses comparing the performance of these weighting methods based on empirical and simulated data using different quality measures. The aim is to identify weighting methods that could be recommended for use in each situation (depending on the available information) or the missing comparison analyses that should be carried out to arrive at a recommendation. We conclude that in the case of additional information in the form of a semantic scale, the cardinal sum reciprocal method can definitively be recommended. However, when only ordinal information is provided by the DM and in cases where additional information is provided in the form of precise/imprecise cardinal information or a ranking of differences, although there are some outstanding methods, further comparison analysis should be carried out to recommend a weighting method.

BMSeNet: Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network for Real-Time Semantic Segmentation.

Most real-time semantic segmentation networks use shallow architectures to achieve fast inference speeds. This approach, however, limits a network's receptive field. Concurrently, feature information extraction is restricted to a single scale, which reduces the network's ability to generalize and maintain robustness. Furthermore, loss of image spatial details negatively impacts segmentation accuracy. To address these limitations, this paper proposes a Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network (BMSeNet). First, to address the limitation of singular semantic feature scales, a Multiscale Context Pyramid Pooling Module (MSCPPM) is introduced. By leveraging various pooling operations, this module efficiently enlarges the receptive field and better aggregates multiscale contextual information. Moreover, a Spatial Detail Enhancement Module (SDEM) is designed, to effectively compensate for lost spatial detail information and significantly enhance the perception of spatial details. Finally, a Bilateral Attention Fusion Module (BAFM) is proposed. This module leverages pixel positional correlations to guide the network in assigning appropriate weights to the features extracted from the two branches, effectively merging the feature information of both branches. Extensive experiments were conducted on the Cityscapes and CamVid datasets. Experimental results show that the proposed BMSeNet achieves a good balance between inference speed and segmentation accuracy, outperforming some state-of-the-art real-time semantic segmentation methods.

Cognitive differences in product shape evaluation between real settings and virtual reality: case study of two-wheel electric vehicles

Product shape evaluation is an important part of new product development. In the shape design stage, design schemes are often presented through visual images. The presentation of visual images causes evaluators to form different cognitive experiences and evaluation results. In recent years, virtual reality (VR) technology has been widely used in the field of industrial design, enriching the presentation forms of design scheme images. Although VR technology has shown the potential to improve evaluators’ perception and cognitive experiences in product shape design, research comparing it with traditional methods remains relatively scattered. This study used two-wheel electric vehicles as an example to examine the difference in evaluators’ cognition of product shape in VR and a real setting (RS). First, we established a semantic scale comprising seven pairs of opposite adjectives to evaluate the shape scheme. Second, we built VR and RS evaluation environments using head-mounted displays and paper renderings, respectively. The participants evaluated the vehicle shape design in alternating viewing and underwent semi-structured interviews on cognitive experience. We analyzed the experimental and interview results based on three aspects of product shape cognition. The results demonstrated that volume cognition was significantly more accurate in VR environments. Furthermore, graphic cognition, particularly regarding shape details, differed partially between environments. VR provided a better sense of immersion and more variable viewing angles than RS. Treatment cognition did not exhibit significant differences between environments, as it depended on human experience rather than visualization. These findings suggest that VR tools are more suited for shaping design evaluations early. Selecting suitable visual presentation tools based on evaluators’ cognitive characteristics at different evaluation nodes to display design schemes is a practical, economical, and efficient strategy.

Linguo-cultural value of 忍 ‘patience’ in traditional and modern Chinese culture

The article outlines a gap in the comprehensive study of values and evaluation processes in human consciousness, emphasising how these processes are aligned with linguo-cultural concepts. The research aims to investigate the evolving cultural value of 忍 (rěn) ‘patience’ in Chinese culture, highlighting the significance of understanding these transformations in the context of axiological linguistics. A mix of qualitative and quantitative methods to explore the cultural and linguistic significance of 忍 (rěn) in Chinese culture was employed. Lexicographical, contextual, and semantic analyses, along with cultural commentary, were applied to describe the multifaceted meanings of 忍 (rěn). The study also used a two-part experimental approach: first, a survey collected diverse Chinese perceptions of 忍 (rěn) through adjectives/nouns, and then an evaluative experiment with bipolar semantic scales measured attitudes towards patience along various dimensions. The findings point out shifts in the modern interpretation of忍 (rěn) within Chinese culture. While traditional values emphasise endurance and resilience, contemporary perspectives acknowledge the complexity of patience, recognising its strategic, interpersonal, and ethical dimensions. The concept of patience is evolving to encompass adaptability, compromise, and long-term vision, reflecting changing societal values and the influence of globalisation. Despite these shifts, the significance of 忍 (rěn) in fostering personal growth, social harmony, and ethical conduct remains central to contemporary Chinese values and societal norms. The results contribute to a deeper understanding of the transformation of cultural values within society, illustrating how linguistic methods can be employed to study their evolving nature.

Genome-Wide Epistatic Network Analyses of Semantic Fluency in Older Adults.

Semantic fluency impairment has been attributed to a wide range of neurocognitive and psychiatric conditions, especially in the older population. Moderate heritability estimates on semantic fluency were obtained from both twin and family-based studies suggesting genetic contributions to the observed variation across individuals. Currently, effort in identifying the genetic variants underlying the heritability estimates for this complex trait remains scarce. Using the semantic fluency scale and genome-wide SNP genotype data from the Long Life Family Study (LLFS), we performed a genome-wide association study (GWAS) and epistasis network analysis on semantic fluency in 2289 individuals aged over 60 years from the American LLFS cohorts and replicated the findings in 1129 individuals aged over 50 years from the Danish LLFS cohort. In the GWAS, two SNPs with genome-wide significance (rs3749683, p = 2.52 × 10-8; rs880179, p = 4.83 × 10-8) mapped to the CMYAS gene on chromosome 5 were detected. The epistasis network analysis identified five modules as significant (4.16 × 10-5 < p < 7.35 × 10-3), of which two were replicated (p < 3.10 × 10-3). These two modules revealed significant enrichment of tissue-specific gene expression in brain tissues and high enrichment of GWAS catalog traits, e.g., obesity-related traits, blood pressure, chronotype, sleep duration, and brain structure, that have been reported to associate with verbal performance in epidemiological studies. Our results suggest high tissue specificity of genetic regulation of gene expression in brain tissues with epistatic SNP networks functioning jointly in modifying individual verbal ability and cognitive performance.

Iconicity mediates semantic networks of sound symbolisma).

One speech sound can be associated with multiple meanings through iconicity, indexicality, and/or systematicity. It was not until recently that this "pluripotentiality" of sound symbolism attracted serious attention, and it remains uninvestigated how pluripotentiality may arise. In the current study, Japanese, Korean, Mandarin, and English speakers rated unfamiliar jewel names on three semantic scales: size, brightness, and hardness. The results showed language-specific and cross-linguistically shared pluripotential sound symbolism. Japanese speakers associated voiced stops with large and dark jewels, whereas Mandarin speakers associated [i] with small and bright jewels. Japanese, Mandarin, and English speakers also associated lip rounding with darkness and softness. These sound-symbolic meanings are unlikely to be obtained through metaphorical or metonymical extension, nor are they reported to colexify. Notably, in a purely semantic network without the mediation of lip rounding, softness can instead be associated with brightness, as illustrated by synesthetic metaphors such as yawaraka-na hizashi /jawaɾakanaçizaɕi/ "a gentle (lit. soft) sunshine" in Japanese. These findings suggest that the semantic networks of sound symbolism may not coincide with those of metaphor or metonymy. The current study summarizes the findings in the form of (phono)semantic maps to facilitate cross-linguistic comparisons of pluripotential sound symbolism.

Learning Multi-Scale Video-Text Correspondence for Weakly Supervised Temporal Article Gronding

Weakly Supervised temporal Article Grounding (WSAG) is a challenging and practical task in video understanding. Specifically, given a video and a relevant article, whose sentences are at different semantic scales, WSAG aims to localize corresponding video segments for all “groundable” sentences. Compared to other grounding tasks, e.g., localizing one target segment with respect to a given sentence query, WSAG confronts an essential obstacle rooted in the intricate multi-scale information inherent within both textual and visual modalities. Existing methods overlook the modeling and alignment of such structured information present in multi-scale video segments and hierarchical textual content. To this end, we propose a Multi-Scale Video-Text Correspondence Learning (MVTCL) framework, which enhances the grounding performance in complex scenes by modeling multi-scale semantic correspondence both within and between modalities. Specifically, MVTCL initially aggregates video content spanning distinct temporal scales and leverages hierarchical textual relationships in both temporal and semantic dimensions via a semantic calibration module. Then multi-scale contrastive learning module is introduced to generate more discriminative representations by selecting typical contexts and performing inter-video contrastive learning. Through the multi-scale semantic calibration architecture and supervision design, our method achieves new state-of-the-art performance on existing WSAG benchmarks.

Applying a Genetic Algorithm to Implement the Fuzzy-MACBETH Method in Decision-Making Processes

This paper describes the development of an evolutionary algorithm for building cardinal scales based on the Fuzzy-MACBETH method. This method uses a triangular fuzzy numbers scale in the MACBETH method to incorporate the subjectivity of a semantic scale into mathematical modeling, which enables circumventing the cardinal inconsistency problem of the classical method, facilitating its application in complex contexts. A genetic algorithm is used in the fuzzy system developed here to build the basic fuzzy scale in a cardinally inconsistent decision matrix. The proposed technique is inspired by crossover and mutation genetic operations to explore potential solutions and obtain a cardinal scale aligned with the decision maker’s preferences. Finally, an illustrative example of the application of the proposed decision support system is presented. The results confirm that the FGA-MACBETH method aligns with the classical method. This study’s primary contribution is that circumventing the problem of cardinal inconsistency in a semantically consistent decision matrix enabled obtaining a cardinal scale without requiring the decision maker to redo his/her initial assessments.

Identification of Predictors of a Meaningful Avoidance Strategy

Introduction. In this section the authors present the relevance of the study of the semantic avoidance strategy which is maladaptive when coping with crisis and uncertain situations. We consider the assumption of the presence of predictors of a sense-forming avoidance strategy as a hypothesis that has significant correlations with the scales of the questionnaire for the study of avoidance.Objective. Presentation of the results of empirical identification of predictors of a meaningful avoidance strategy.Materials and methods. The author’s verbal questionnaire is presented and applied to study the avoidance strategy. It consists of 9 semantic scales of personality traits to study the specifics of avoidance. To identify its predictors, the battery of tests includes proven methods from the related fields of semantic regulation: the test of life orientations (D. A. Leontiev), the “New Questionnaire of tolerance-tolerance to uncertainty” (T. V. Kornilova).Results. The indicators for verifying the normality of the distribution of empirical indicators are presented as well as data from the correlation analysis of the battery of tests in the sample n = 282. Empirically established values of the Shapiro-Wilk coefficient have values higher than the required level of statistical significance ,05 for indicators of all scales of this battery of tests. Correlation analysis has established that there are three indicators of the scales of the tested tests, which have values of statistically significant empirical Pearson correlation coefficients with the scales of the author’s questionnaire.Discussion. Empirically calculated values of the Shapiro-Wilk criterion with respect was based on the satisfaction to a specified level of statistical significance ,05 it was found that all the studied indicators have a normal distribution character. This confirms the representative nature of the sample. According to the results of the correlation analysis conducted at the significance level ,05, the scales “process” and “locus of control – I” of the test “Life orientations” (D. A. Leontiev) were adopted as the main predictors of avoidance, and the scale “tolerance to uncertainty” according to the “New questionnaire of tolerance to uncertainty” was used as an auxiliary predictor (T. V. Kornilova). The stated hypothesis has been confirmed. Limitations of the study – testing did not affect respondents under the age of 18, as well as over the age of 47, and was conducted only for the types of profession “human – human” and “human – sign”. It is possible to conduct low-cost testing and build multifactorial personal avoidance profiles with the help of the identified predictors.

Attention-guided multi-granularity fusion model for video summarization

Video summarization has attracted extensive attention benefiting from its valuable capability to facilitate video browsing. While achieving notable improvement, existing methods still fail to sufficiently and effectively model contextual information within videos, hindering the summarization performance owing to a deficiency in powerful contextual representations. To address this limitation, we present a novel Attention-Guided Multi-Granularity Fusion Model (AMFM), which allows for optimizing the modeling process from the context capturing and fusion perspective. AMFM comprises three dominant components including a content-aware enhancement (CAE) module, a multi-granularity encoder (MGE), and a scale-adaptive fusion (SAF) module. More specifically, CAE dynamically enhances pre-trained visual features by learning the potential visual relationship across frame-level and video-level embeddings. Subsequently, coarse-grained and fine-grained contextual information is simultaneously modeled in the same representation space by MGE with the combination of self-attention and temporal convolution scheme. Furthermore, the multi-granularity representations with a significant difference in the semantic scale are adaptively fused by SAF. Our method can precisely pinpoint key segments by effectively modeling and processing rich temporal representations. Extensive comparisons with state-of-the-art methods on standard datasets demonstrate the effectiveness of the proposed method, and the ablation studies further verify the positive impact of each module in our model.

Non-Financial Feasibility Analysis on The Utilization of Cattle Livestock Waste as a Biogas

This research aims to analyze the non-financial feasibility of using livestock waste as raw material for biogas. Qualitative data was obtained to provide an overview of the cattle farming business, the implementation of waste processing with a biogas installation, where the final result is biogas which can be utilized by livestock breeder groups and the community around the installation. The feasibility of this activity is determined using the differential semantic scale formula. The results of the non-financial feasibility analysis show that the business of using livestock waste as raw material for biogas is feasible to develop in terms of market aspects, technical aspects, management aspects, as well as social and environmental aspects. The two biogas installations granted by the regional government were able to help the community save energy.

Kesiapan Tenaga Administrasi Sekolah Menerima Perubahan pada SMP Negeri Se Kota Padang

This research is based on the results of an interview with the head of school administration staff at SMP Negeri Kota Padang. This study aims to obtain information about the readiness of school administration personnel to accept change in terms of three aspects, namely, mental readiness, knowledge readiness and participation in change. This research is a descriptive quantitative research. The population in this study is all school staff in the city of Padang. The number of samples in this study was 83 people. The instrument used in the questionnaire is a semantic differential semantic scale. The steps taken in analyzing data are, verifying data, tabulating data, calculating the average value (mean) then looking for the percentage of score achievement and describing the data processed based on specified criteria. This study shows the results of the readiness of administrative personnel to accept changes in public junior high schools in Padang City as seen from the indicators (1) Mental readiness with the ready category and 80.10% achievement rate, (2) Knowledge readiness with the ready category and 81.02% achievement rate, (3) Participation in changes with the ready category and 80.19% achievement rate. It can be concluded that the readiness of school administration personnel in accepting changes in public junior high schools in Padang City can be categorized as ready to accept changes with an achievement level (8, 0.44%).

Architectonic Design Supported by Visual Environmental Simulation—A Comparison of Displays and Formats

Visual environmental simulations are fundamental in understanding the relationship between the built environment and psychological perception. The remarkable evolution of virtual immersion displays over recent years has provided a series of advantages to the architectural discipline, one of which is that non-specialists now have the potential to better understand architectural spaces. This work aimed to analyse the adequacy of the main displays and formats currently used in environmental simulations. As the objective was twofold, two experimental studies were carried out (with a sample of 100 participants). The studies evaluated users’ responses to different environmental representations of two environments, using differential semantic scales to measure key underlying factors (utility, credibility, realism, accuracy, abstraction). The first study examined simulation displays: a PC, an HTC Vive Pro 2 head-mounted display, a PowerWall Screen and a CAVE. In the second, formats were analysed: normal image, 360° image, video and 360° video. The results of this work revealed that users perceived the space differently depending on the representation displays and formats used. Such comparisons of these new means of representing architectural spaces can be helpful to researchers, architects and urban planning professionals and might provoke debate in, and be extrapolated into, the design field.

An Examination of the Relationship Between Visualization Media and Consumer Product Evaluation.

Virtual product presentations that rely on static images and text are often insufficient to communicate all the information that is necessary to accurately evaluate a product. Technologies such as Virtual Reality (VR) or Augmented Reality (AR) have enabled more sophisticated representation methods, but certain product characteristics are difficult to assess and may result in perceptual differences when a product is evaluated in different visual media. In this article, we report two case studies in which a group of participants evaluated three designs of two product typologies (i.e., a desktop telephone and a coffee maker) as presented in three different visual media (i.e., photorealistic renderings, AR, and VR for the first case study; and photographs, a non-immersive virtual environment, and AR for the second case study) using eight semantic scales. An inferential statistical method using Aligned Rank Transform (ART) proceedings was applied to determine perceptual differences between groups. Our results show that in both cases product attributes in Jordan's physio-pleasure category are the most affected by the presentation media. The socio-pleasure category was also affected for the case of the coffee makers. The level of immersion afforded by the medium significantly affects product evaluation.

Research on Product Design of Face Pareidolia

Pareidolia means that the brain sees a meaningful shape in a meaningless thing when it receives a blurred picture from the outside world. Face Pareidolia has become an effective design method in the field of design to enhance product interest, attract attention, and convey emotional characteristics. This study’s purpose is to explore whether there are differences in product design with pareidolia in different categories, and how the appearance of emotional faces affects product perception. Shopee, MOMO, and PChome 24h are the top three shopping websites in Taiwan. Used as the search platform for the number of product samples. Products with facial pareidolia (Face Pareidolia) are classified into three main categories: household appliances, 3C audio-visual, and home decoration accessories. From each category, select 8 photos of products with fantasy facial features. Scored by the Product Semantic Difference Scale. After collecting 93 valid questionnaires. Among the three product types with application illusions, the 3C audio-visual category presents a more technological sense of product tonality. And there are obvious differences with the other two types of products. Emotional appearance design can have an impact on the semantics of the product, and strengthen the perception of 「seriousness, toughness, coldness, and sense of technology」, while its 「interestingness and delicacy」 are less affected by emotional appearance.

High frequency medical Spanish terminology: A corpus-based study of textbooks and reference materials

The purpose of this study is to identify high frequency medical Spanish terminology by building a corpus of medical Spanish textbooks and resources. By discovering the most common terms, instructors and curriculum designers can include salient topics in their courses, which will better serve the learners in these courses. To do so, medical Spanish textbooks and resources from 2012-2022 were uploaded to the corpus software AntConc. The final corpus consisted in a total of 26 resources and 1,421,163 tokens (or words). Word frequencies were calculated, and the top 3,000 most frequent terms were determined. A Semantic Rating Scale (Chung &amp; Nation, 2003; Quero &amp; Coxhead, 2018) was used to categorize words as general or technical. The top 3,000 words covered 38.9% of the entire corpus, and the most frequent words found in the medical Spanish resources were general Spanish vocabulary at 68.2%. The remaining 31.8% were medical Spanish technical terminology. The generation of these lists gives instructors and curriculum designers a tool to use in the development of medical Spanish courses that when combined with a patient-centered, communicative medical Spanish curriculum has the potential to enhance learners’ medical Spanish proficiency.