Semantic Measures Research Articles

The many guises of productivity: a case-study of Spanish inchoative constructions

Abstract This study investigates the productivity of a family of inchoative constructions in (Peninsular) Spanish (empezar a + inf., echar a + inf.) and the way it correlates with semantic openness. Productivity is defined as lexical openness of a slot (here the infinitive) of a construction. A range of productivity measures (e.g., type/token ratio, hapax/token ratio, and other parameters related to the frequency of the most frequent types) are discussed and compared. Semantic openness is operationalized on the basis of distributional semantics, yielding two measures viz. semantic range and semantic sparsity. The correlations between productivity measures and semantics are studied by means of a Principal Components Analysis. It is shown that the so-called ‘anti-productivity’ measures (related to entrenchment due to high token frequency) on the one hand, and the productivity measures (including the hapax-based ones, traditionally associated with extensibility) on the other hand, strongly correlate and constitute opposites along the same dimension of lexical openness. In addition, the semantic measures strongly correlate with productivity, which suggests that often semantic and lexical openness go hand in hand in this dataset. As to the behavior of the individual auxiliaries (= micro-constructions), they appear to be rather extreme, showing very productive and less productive profiles.

Joint embedding in hierarchical distance and semantic representation learning for link prediction

The link prediction task aims to predict missing entities or relations in the knowledge graph and is essential for the downstream application. Existing well-known models deal with this task by mainly focusing on representing knowledge graph triplets in the distance space or semantic space. However, they can not fully capture the information of head and tail entities, nor even make good use of hierarchical level information. Thus, in this paper, we propose a novel knowledge graph embedding model for the link prediction task, namely, HIE, which models each triplet (h, r, t) into distance measurement space and semantic measurement space, simultaneously. Moreover, HIE is introduced into hierarchical-aware space to leverage rich hierarchical information of entities and relations for better representation learning. Specifically, we apply distance transformation operation on the head entity in distance space to obtain the tail entity instead of translation-based or rotation-based approaches. Experimental results of HIE on four real-world datasets show that HIE outperforms several existing state-of-the-art knowledge graph embedding methods on the link prediction task and deals with complex relations accurately.

Investigating morphosyntactic and semantic measures in bilingual Azeri-Persian speaking children aged 5.5 to 6.5 years with and without language impairment

The present study aims to investigate morphosyntactic and semantic measures in bilingual Azeri-Persian-speaking children aged 5.5–6.5 years with and without language impairment. In this cross-sectional study, the bilingual participants were thirty children with language impairment (LI) and fifteen typically developing children (TD) who were selected from nurseries and Speech therapy clinics. The language samples were collected through story-telling in Azeri and Persian languages, separately. The linguistic analysis was done based on morphosyntactic and semantic parameters. The Alberta Language and Development Questionnaire (ALDeQ) parent report questionnaire was completed via interviewing with the parents to differentiate language impairment from language differences. Study findings revealed a significant difference between the morphosyntactic and semantic scores in two groups of bilingual Azeri-Persian speaking LI and TD children (p ˂ 0.05). Also, the results demonstrated no significant relationship between the scores of linguistic scores and age in LI and TD children (p < 0.05). According to the result of the study, morphosyntactic and semantic parameters of language samples in bilingual Azeri-Persian-speaking children could be utilized to provide diagnostic information for speech and language pathologists in LI children among bilingual Azeri-Persian communities.

Semantic feature salience matters: new values of significance and relative weight for the Spanish feature norms for young and older adults

AbstractTo determine the semantic importance of certain features has been a topic of interest for many semantic memory models. Diverse measures have been proposed in the last years. Semantic significance, the latest measure proposed by Montefinese and colleagues, is both sophisticated and comprehensive. Given the cultural and linguistic variability of semantic measures, this study presents values of significance and relative weight for the Spanish-speaking population corresponding to 400 concrete concepts. First, we presented data for both young and older adults. Second, we assessed the effect of significance on response times in two speeded feature verification tasks. Third, we compared significance values with the existing Italian significance norms. To evaluate the effect of significance to predict response times, two speeded verification tasks (Experiment 1 and 2) were carried out, selecting a total of a 130 concepts for analysis. In Experiment 1, subjects were presented with a feature followed by a concept, while in Experiment 2, the order of stimulus presentation was reversed (i.e., the concept was presented before the feature). An independent linear mixed model showed that significance was a good predictor of response latencies in Experiments 1 and 2. Moreover, results revealed a strong positive correlation between the Spanish and Italian significance values. Findings are discussed in terms of recent theories of semantic cognition.

The principal components of meaning, revisited.

Osgood, Suci, and Tannebaum were the first to attempt to identify the principal components of semantics using dimensional reduction of a high-dimensional model of semantics constructed from human judgments of word relatedness. Modern word-embedding models analyze patterns of words to construct higher dimensional models of semantics that can be similarly subjected to dimensional reduction. Hollis and Westbury characterized the first eight principal components (PCs) of a word-embedding model by correlating them with several well-known lexical measures, such as logged word frequency, age of acquisition, valence, arousal, dominance, and concreteness. The results show some clear differentiation of interpretation between the PCs. Here, we extend this work by analyzing a larger word-embedding matrix using semantic measures initially derived from subjective inspection of the PCs. We then use quantitative analysis to confirm the utility of these subjective measures for predicting PC values and cross-validate them on two word-embedding matrices developed on distinct corpora. Several semantic and word class measures are strongly predictive of early PC values, including first-person and second-person verbs, personal relevance of abstract and concrete words, affect terms, and names of places and people. The predictors of the lowest magnitude PCs generalized well to word-embedding matrices constructed from separate corpora, including matrices constructed using different word-embedding methods. The predictive categories we describe are consistent with Wittgenstein's argument that an autonomous level of social interaction grounds linguistic meaning.

Open Brain AI and language assessment.

Neurolinguistic assessments play a vital role in neurological examinations, revealing a wide range of language and communication impairments associated with developmental disorders and acquired neurological conditions. Yet, a thorough neurolinguistic assessment is time-consuming and laborious and takes valuable resources from other tasks. To empower clinicians, healthcare providers, and researchers, we have developed Open Brain AI (OBAI). The aim of this computational platform is twofold. First, it aims to provide advanced AI tools to facilitate spoken and written language analysis, automate the analysis process, and reduce the workload associated with time-consuming tasks. The platform currently incorporates multilingual tools for English, Danish, Dutch, Finnish, French, German, Greek, Italian, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, and Swedish. The tools involve models for (i) audio transcription, (ii) automatic translation, (iii) grammar error correction, (iv) transcription to the International Phonetic Alphabet, (v) readability scoring, (vi) phonology, morphology, syntax, semantic measures (e.g., counts and proportions), and lexical measures. Second, it aims to support clinicians in conducting their research and automating everyday tasks with "OBAI Companion," an AI language assistant that facilitates language processing, such as structuring, summarizing, and editing texts. OBAI also provides tools for automating spelling and phonology scoring. This paper reviews OBAI's underlying architectures and applications and shows how OBAI can help professionals focus on higher-value activities, such as therapeutic interventions.

Workload Analysis and Green Human Resource Management on Employee Performance

The manufacturing industry sector is an industrial sector in Indonesia that is developing and contributes to the industry nationally, therefore companies engaged in this field must be able to face competition which is the impact of the growing industry. So that human resources are an important part of the company, if the human resources are lacking, the company's target in achieving its goals will not be carried out PT. ASTRA INTERNATIONAL Tbk Isuzu Sukabumi - Bogor. Tbk Sukbumi -Bogor is a company engaged in the automotive manufacturing industry that has problems with employee performance. This study aims to find out whether workload and green human resource management have an influence and impact on employee performance at PT. ASTRA INTERNATIONAL Tbk Isuzu Sukabumi - Bogor. The research method used is a quantitative method using descriptive and associative methods. The data source used by conducting interviews and distributing questionnaires as a tool in obtaining data and samples was used namely saturated sampling to 31 respondents of PT. ASTRA INTERNATIONAL Tbk Isuzu Sukabumi - Bogor by using a differential semantic measurement system. Then the data that has been obtained is carried out several tests such as validity and reality tests, then a classical assumption test is carried out, which is then analyzed with the help of SPPS 23 software to answer the hypothesis in this study. The results of the study show that there is a positive and significant influence between workload and green human resource management on employee performance and by reducing workload and implementing green human resource management can improve the performance of employees of PT. ASTRA INTERNATIONAL Tbk Isuzu Sukabumi - Bogor.

EVALUATION OF SEMANTIC SIMILARITY BETWEEN GENE ONTOLOGY BASED ON PROTEIN FAMILY AND PATHWAY ANALYSIS

An evolving vocabulary that explains the roles of proteins and genes is called gene ontology, or GO. Gene ontology (GO) describes the molecular, cellular, and biological levels of gene functioning. Semantic similarity gained relevance due to the widespread usage of gene annotations. There are a number of semantic similarity metrics that are available in the literature that concentrate on various strategies: distance-based techniques at the word level or gene product level, external documents, topology-based approaches that focus on boundaries, ancestor or child nodes. We presume that combining all of these elements results in a methodical way to gauge the degree of similarity across GO annotation items. We have conducted a detailed analysis of the biological pathways and GO keywords, and we have created a semantic measure of similarity called SimGOT. SimGOT takes into account topology-based similarity measures, membership of words in fuzzy clustering, and semantics hidden in the ontology or information content of a term. UniProt is used to build the datasets that are positive and negative. We compared four existing GO-based semantic similarity metrics based on semantic similarity, Pearsons correlation coefficient, and Protein Family (Pfam) subdomain group similarity. The superiority of SimGOT over alternative semantic similarity metrics is demonstrated by the experimental findings. KEYWORDS—Gene Ontology, Pearsons correlation coefficient, Protein family, SimGOT, UniProt

A semantic-driven coupled network for infrared and visible image fusion

In order to be adapted to high-level vision tasks, several infrared and visible image fusion methods cascade with the downstream network to enhance the semantic information of fusion results. However, due to the feature-level heterogeneities between fusion and downstream tasks, these methods suffer from the loss of pixel-level information and incomplete reconstruction of semantic-level information. To further improve the performance of fusion images in high-level vision tasks, we propose a semantic-driven coupled network for infrared and visible image fusion, terms as SDCFusion. Firstly, to address feature heterogeneity, we couple the segmentation and fusion networks into a joint framework such that both networks share the multi-level cross-modality coupled features. Based on the joint optimization of dual tasks, a joint action between fusion and downstream tasks is formed to force the cross-modality coupled features modeled on both pixel domain and semantic domain. Subsequently, to guide the semantic information reconstruction, we cascade two networks to form the semantic-based driven action, which continuously optimizes the fusion image to achieve semantic representation capacity. In addition, we introduce an adaptive training strategy to reduce the complexity of dual-task training. Specifically, an mIoU-based semantic measurement weight is designed to balance the joint action and driven action throughout the training process. We evaluate our method at both pixel information and semantic information levels, respectively. The qualitative and quantitative experiments verify the superiority of SDCFusion in terms of visual effects and metrics. The object detection and semantic segmentation experiments demonstrate that SDCFusion achieves superior performance in high-level vision tasks. The source code is available at https://github.com/XiaoW-Liu/SDCFusion.

Navigating the semantic space: Unraveling the structure of meaning in psychosis using different computational language models

Speech in psychosis has long been ascribed as involving ‘loosening of associations’. We pursued the aim to elucidate its underlying cognitive mechanisms by analysing picture descriptions from 94 subjects (29 healthy controls, 18 participants at clinical high risk, 29 with first-episode psychosis, and 18 with chronic schizophrenia), using five language models with different computational architectures: FastText, which represents meaning non-contextually/statically; BERT, which represents contextual meaning sensitive to grammar and context; Infersent and SBERT, which provide sentential representations; and CLIP, which evaluates speech relative to a visual stimulus. These models were used to quantify semantic distances crossed between successive tokens/sentences, and semantic perplexity indicating unexpectedness in continuations. Results showed that, among patients, semantic similarity increased when measured with FastText, Infersent, and SBERT, while it decreased with CLIP and BERT. Higher perplexity was observed in first-episode psychosis. Static semantic measures were associated with clinically measured impoverishment of thought and referential semantic measures with disorganization. These patterns indicate a shrinking conceptual semantic space as represented by static language models, which co-occurs with a widening in the referential semantic space as represented by contextual models. This duality underlines the need to separate these two forms of meaning for understanding mechanisms involved in semantic change in psychosis.

Video-PSG: An Intelligent Contactless Monitoring System for Sleep Staging.

Polysomnography (PSG) is the gold standard for sleep staging in clinics, but its skin-contact nature makes it uncomfortable and inconvenient to use for long-term sleep monitoring. As a complementary part of PSG, the video cameras are not utilized to their full potential, only for manual check of simple sleep events, thereby ignoring the potential for physiological and semantic measurement. This leads to a pivotal research question: Can camera be used for sleep staging, and to what extent? We developed a camera-based contactless sleep staging system in the Institute of Respiratory Diseases and created a clinical video dataset of 20 adults. The camera-based feature set, derived from both physiological signals (pulse and breath) and motions all measured from a video, was evaluated for 4-class sleep staging (Wake-REM-Light-Deep). Three optimization strategies were proposed to enhance the sleep staging accuracy: using motion metrics to prune measurement outliers, creating a more personalized model based on the baseline calibration of waking-stage physiological signals, and deriving a specialized feature for REM detection. It achieved the best accuracy of 73.1% (kappa = 0.62, F1-score = 0.75) in the benchmark of five sleep-staging classifiers. Notably, the system exhibited high accuracy in predicting the overall sleep structure and subtle changes between different sleep stages. The study demonstrates that camera-based contactless sleep staging is a new value stream for sleep medicine, which also provides clinical and technical insights for future optimization and implementation.

Dynamic semantic networks for exploration of creative thinking

Abstract Human creativity originates from brain cortical networks that are specialized in idea generation, processing, and evaluation. The concurrent verbalization of our inner thoughts during the execution of a design task enables the use of dynamic semantic networks as a tool for investigating, evaluating, and monitoring creative thought. The primary advantage of using lexical databases such as WordNet for reproducible information-theoretic quantification of convergence or divergence of design ideas in creative problem solving is the simultaneous handling of both words and meanings, which enables interpretation of the constructed dynamic semantic networks in terms of underlying functionally active brain cortical regions involved in concept comprehension and production. In this study, the quantitative dynamics of semantic measures computed with a moving time window is investigated empirically in the DTRS10 dataset with design review conversations and detected divergent thinking is shown to predict success of design ideas. Thus, dynamic semantic networks present an opportunity for real-time computer-assisted detection of critical events during creative problem solving, with the goal of employing this knowledge to artificially augment human creativity.

Adaptation of Gap Predictions in Filler-Gap Dependency Processing during Reading

Syntactic adaptation effects have been demonstrated for an expanding list of structure types, but the mechanism underlying this effect is still being explored. In the current work on filler-gap dependency processing, we examined whether exposing participants to a less common gap location—prepositional object (PO) gaps—altered their gap predictions, and whether these effects would transfer across tasks when this input was presented in a quasi-naturalistic way (i.e., by reading stories). In Experiment 1, we demonstrated that comprehenders dampened their direct object (DO) gap predictions following exposure to PO gaps. However, Experiments 2A and 2B suggest that these adaptation effects did not transfer when the quasi-naturalistic exposure phase was presented as a separate task (Experiment 2A) and when they also needed to generalize from a syntactic to a semantic measure of direct object gap predictions (i.e., filled gap vs. plausibility mismatch sentences; Experiment 2B). Overall, these experiments add filler-gap dependency processing, as well as the gap predictions associated with it, to the growing list of structures demonstrating adaptation effects, while also suggesting that this effect may be specific to a singular experimental task environment.

Performance of Agricultural Extension Services (BPP) in Enhancing Rice Production in North Sumatra, Indonesia

Indonesia has experienced an increase in national population, the Government is targeting rice production of 54.50 million tons in 2023. North Sumatra Province has the largest rice production on the island of Sumatra, 2,088,584 tons. The effectiveness of the Agricultural Extension Service (BPP) in Percut Sei Tuan is of concern to researchers. This research method uses a differential semantic measurement scale or attitude scale to analyze performance data. The number of samples determined in the research was 10 farmers per village so that the number of samples from all villages was 30 rice farmers with 5-point rating scale and the choices "Very Good" to "Very Poor" with the interval scale formula {a(m-n)}/b and the number of question attributes is 12 points, and the class interval is 9.6. The results performance is in the "Very Good" performance category with an average score of 4.24. However, based on their respective scores of 3.03, there is a low score of 2.53 in the category of agricultural extension officers who do not assist farmers in obtaining agricultural financing, nor do they help market their agricultural products. Extension officers have carried out but are still not optimal with answers from one research category.

Interdisciplinary college curriculum and its labor market implications

This article sheds light on how to capture knowledge integration dynamics in college course content, improves and enriches the definition and measurement of interdisciplinarity, and expands the scope of research on the benefits of interdisciplinarity to postcollege outcomes. We distinguish between what higher education institutions claim regarding interdisciplinarity and what they appear to actually do. We focus on the core academic element of student experience-the courses they take, develop a text-based semantic measure of interdisciplinarity in college curriculum, and test its relationship to average earnings of graduates from different types of schools of higher education. We observe that greater exposure to interdisciplinarity-especially for science majors-is associated with increased earnings after college graduation.

Waktu Kerja Fleksibel Dan Supervisi Dalam Meningkatkan Kinerja Karyawan Studi: Sales Eksekutif Pt. Theta Nusantara Jaya Daerah Cianjur Dan Sukabumi

This study aims to determine whether the application of flexible working time and supervision has an influence and can improve the performance of executive sales employees at PT. Theta Nusantara Jaya Cianjur and Sukabumi areas. The research method used is a quantitative method. The sample in this study were 42 respondents who are sales executives of PT. Theta Nusantara Jaya Cianjur and Sukabumi areas using saturated sampling technique. Data were obtained through a research questionnairewhich was distributed to all samples containing 24 servers regarding the variables of flexible working time, supervision and employee performance, using a differential semantic measurement system. Furthermore, the data that has been obtained will be analyzed with various tests to answer the hypothesis in this study. The results showed that there is a positive and sinificant influence between monitoring variables on employee performance and simultaneously the application of flexible working time and supervision can improve the performance of executive sales employees at PT. Theta Nusantara Jaya Cianjur and Sukabumi areas.

Assessment of depression and anxiety in young and old with a question-based computational language approach

Middle aged adults experience depression and anxiety differently than younger adults. Age may affect life circumstances, depending on accessibility of social connections, jobs, physical health, etc, as these factors influence the prevalence and symptomatology. Depression and anxiety are typically measured using rating scales; however, recent research suggests that such symptoms can be assessed by open-ended questions that are analysed by question-based computational language assessments (QCLA). Here, we study middle aged and younger adults’ responses about their mental health using open-ended questions and rating scales about their mental health. We then analyse their responses with computational methods based on natural language processing (NLP). The results demonstrate that: (1) middle aged adults describe their mental health differently compared to younger adults; (2) where, for example, middle aged adults emphasise depression and loneliness whereas young adults list anxiety and financial concerns; (3) different semantic models are warranted for younger and middle aged adults; (4) compared to young participants, the middle aged participants described their mental health more accurately with words; (5) middle-aged adults have better mental health than younger adults as measured by semantic measures. In conclusion, NLP combined with machine learning methods may provide new opportunities to identify, model, and describe mental health in middle aged and younger adults and could possibly be applied to the older adults in future research. These semantic measures may provide ecological validity and aid the assessment of mental health.

EFFECTS OF BIOPHILIC RESTORATIVE EXPERIENCES ON DESIGNERS' BODIES, BRAINS, AND MINDS

AbstractThe research presented in this paper explores a novel method for assessing the effects of biophilic restorative experiences on designers’ cognition by combining the use of physiological, neurocognitive and semantic measures. A total of 12 engineering graduate students participated in a three-step pilot experiment that consisted of (1) a stressor (the Trier Social Stress Test), (2) a destressing intervention (biophilic sound experience), and (3) a design task. Heart rate variability (HRV) was used to track subjects’ autonomic nervous system (ANS) activity. Functional near-infrared spectroscopy (fNIRS) was used to track patterns of brain activation in subjects’ prefrontal cortex (PFC). Changes in design quality were assessed by the semantic space they explored, measured through a natural language processing (NLP) technique. Preliminary findings suggest that an auditory biophilic restorative experience can change designers’ bodies, brains, and minds. Results from this pilot study encourage further exploration of the use of exposure to nature-based stimuli as a method to help enhance engineering design cognition.

Quantitative dynamics of design thinking and creativity perspectives in company context

This study is intended to provide in-depth insights into how design thinking and creativity issues are understood and possibly evolve in the course of design discussions in a company context. For that purpose, we use the seminar transcripts of the Design Thinking Research Symposium 12 (DTRS12) dataset “Tech-centred Design Thinking: Perspectives from a Rising Asia,” which are primarily concerned with how Korean companies implement design thinking and what role designers currently play. We employed a novel method of information processing based on constructed dynamic semantic networks to investigate the seminar discussions according to company representatives and company size. We compared the quantitative dynamics in two seminars: the first involved managerial representatives of four companies, and the second involved specialized designers and management of a design center of single company. On the basis of dynamic semantic networks, we quantified the changes in four semantic measures—abstraction, polysemy, information content, and pairwise word similarity—in chronologically reconstructed individual design-thinking processes. Statistical analyses show that design thinking in the seminar with four companies, exhibits significant differences in the dynamics of abstraction, polysemy, and information content, compared to the seminar with the design center of single company. Both the decrease in polysemy and abstraction and the increase in information content in the individual design-thinking processes in the seminar with four companies indicate that design managers are focused on more concrete design issues, with more information and less ambiguous content to the final design product. By contrast, specialized designers manifest more abstract thinking and appear to exhibit a slightly higher level of divergence in their design processes. The results suggest that design thinking and creativity issues are articulated differently depending on designer roles and the company size.

Predicting literacy development and risk in Spanish-English bilingual first graders

Purpose: There are well-established links between oral language and reading development in monolingual English-speaking children that are associated with literacy outcomes. Oral language, defined relative to lexical quality, provides key support for developing early reading skills. For bilingual children, the connection between oral language and reading development and risk for identification of having language or reading difficulties is assumed but has been less systematically studied. Our goal was to document the relationship between knowledge of semantics and morphosyntax and children's performance on letter and word identification in first grade in each of their languages so as to evaluate the risk for poor reading outcomes in the Spanish and English of bilingual first graders. Method: We examined data from 111 Spanish-English bilingual first graders on measures of Spanish and English semantics and morphosyntax in relation to letter-word identification, which indexes early reading development. We evaluated the within- and across-language relationships between skills and also whether the same children who performed in the at-risk range on the oral language tasks showed risk for future reading difficulties based on their letter-word performance. Results: Oral language skills and early reading skills were strongly related within languages, and cross-language relationships were also evident, though not for all domains. There were significant associations between risk for developmental language disorder (DLD) based on semantics performance and reading risk. Conclusions: We observe cross-linguistic relationships between letter-word identification and morphosyntactic and semantic skills in young Spanish-English bilinguals with and without risk for language and reading development. These findings indicate that connections between oral language and reading hold across differing levels of language and reading risk.