Overlapping connectivity patterns during semantic processing of abstract and concrete words revealed with multivariate Granger Causality analysis Scientific Reports

The feature vector of each retelling was computed as the mean distance of all verbs in the retelling to each verb in the corresponding original story. Second, we examined classifier performance based on each retelling’s overall semantic structure, as captured by GloVe embeddings. Numerical representations were obtained for all post-tagged words in each processed retelling. The overall feature vector of each retelling was calculated as the mean word embedding of all its words. In both approaches, classification models were created using support vector machines, with the same cross-validation strategy used in our main analyses.

• The symbol \(\tau\) will refer to a token contained within a processed tweet, where \(\tau _i\) indicates one of many such tokens in any given tweet.
• Four additional columns in the file (Supplementary Table S4), #(0|0), #(1|0), #(0|1), #(1|1), give the number of transitions used to compute the probability for each meaning of a lexeme.
• This division is also reflected in public opinion on the idea of Ukraine joining the EU and NATO.
• This reinforces the view that semantic abnormalities in PD are mainly driven by action concepts.

The third force comes from the “connectivity effect” that results from high-frequency co-occurrences of translation equivalents in the source and the target languages (Halverson, 2017). This hypothesis, which has been used to explain translation universals at the lexical and syntactic levels (Liu et al., 2022; Tirkkonen-Condit, 2004) may also extend its applicability to translation universals at the semantic level. The results of the current study suggest that the influences of both the source and the target languages on the translated language are not solely limited to the lexical and syntactic levels. For our connectivity measure, we adopted a method based on the concept of Granger Causality (GC)49,50 called partial directed coherence (PDC)51. Note that Granger Causality should not be confused with causality in the conceptual sense of “A brings about B”. Rather, it is an estimation of causal statistical influences without the need for a physical intervention52,53.

In relation to the principle of linguistic meaning conservation

There was also a weaker effect with the unrelated/nonword prime group, but this was not significant after correction for multiple comparisons. Pre-processing of the data was done offline using Fieldtrip37 with MATLAB (R2017a). The data was then visually inspected for bad channels, which were reconstructed based on neighbouring channels (this occurred with one subject who had 4 bad channels), and obvious artefacts were removed. Independent component analysis (ICA) was then used to decompose the data (runica algorithm). Components identified by this analysis were visually checked, and any that resembled eye blinks, eye movements, heart beats, impedance, or other movement related artefacts were removed.

Frawley (2000) also introduced a similar concept known as “the third code” to emphasize the uniqueness of translational language generated from the process of rendering coded elements into other codes. The question of whether translational language should be regarded as a distinctive language variant has since sparked considerable debate in the field of translation studies. Although previous natural discourse studies on PD8,20,35 and other neurodegenerative disorders29 have yielded robust results with similar and smaller groups, replications with more participants would ChatGPT be needed. Relatedly, results stemmed from the distance between the original texts’ verbs and the ones produced by participants in each training fold, meaning that they might change if new participants were tested and produced verbs that were not present in such folds. Hence, our models should be enriched with larger samples (ideally allowing for out-of-sample validation) so as to strengthen their generalizability. You can foun additiona information about ai customer service and artificial intelligence and NLP. Second, the AT and nAT we employed described only a few action and non-action events which may not be directly relevant to patients’ daily activities.

What Is Semantic Analysis?

We also tested different approaches, such as subtracting the median and dividing by the interquartile range, which did not yield better results. Contributing to this stream of research, we use a novel indicator of semantic importance to evaluate the possible impact of news on consumers’ confidence. The violin plot below (Fig. 2) shows the distributions of AU-ROC scores for each of the four scalar formulas. The two halves of each distribution correspond to the two values tested for Hidden Layer Dimensionality. The remainder of the parameters appeared to deviate somewhat from the values seen as local maximums in the initial testing. Minimum Word Frequency (MWF) and Word Window Size (WWS) were apparently affected by the simultaneous adjustment of other parameters, as well as being somewhat more influenced by the number of training epochs (EP).

Both ADM and dysplasia are accompanied by a prominent stromal reaction and immune cell infiltrate13. The stages of ADM and dysplasia evolution are believed to encompass a long phase of pre-cancer evolution that is a valuable window for early intervention14. Looking ahead, it is becoming increasingly crucial to establish a universal semantic layer to provide business users with a consistent view of all enterprise data and to enable them to conduct rapid analysis. Creating a semantic layer on top of all data sources ensures quick access to a single source of truth, facilitating a shared understanding of dimensions and metrics across the organization. A well-designed and high-performing semantic layer empowers business users to leverage data more efficiently, delivering actionable insights and driving faster decision-making.

By investigating the connectivity between different brain areas during the processing of concrete and abstract words, it is possible to improve the current neurobiological models for these observations. So far, only a few experimental studies have investigated the functional connectivity for abstract and concrete word processing. Additionally, a stronger connectivity pattern for concrete characteristics was found in both the left and right hemispheres. However, since the study tested imagery rather than an actual reading process, the question remains as to what happens in the brain when abstract or concrete words are read. Furthermore, as the study was done with fMRI, the authors could not describe the spectral and temporal characteristics of the identified networks.

That is to say, translation universals at the syntactic-semantic level, such as explicitation and simplification, can be further distinguished depending on whether the syntactic-semantic feature presents the same or opposite results for S-universal and T-universal. This further suggests that even the translation universal under the same sub-hypothesis, like explicitation as S-universal, can be attributed to different causes. Therefore, further analysis is warranted to distinguish different types of translation universals at the syntactic-semantic level and figure out the underlying causes so that we can better understand translation as a dynamic and complex system (Han & Jiang, 2017; Sang, 2023). However, intriguingly, some features of specific semantic roles show characteristics that are common to both S-universal and T-universal. For example, the frequencies of agents (A0) and discourse markers (DIS) in CT are higher than those in both ES and CO, suggesting that the explicitation in these two roles is both S-oriented and T-oriented.

While the fields GlobalEventID and EventTimeDate are globally unique attributes for each event, MentionSourceName and MentionTimeDate may differ. Based on the GlobalEventID and MentionSourceName fields in the Mention Table, we can count the number of times each media outlet has reported on each event, ultimately constructing a “media-event” matrix. In this matrix, the element at (i, j) denotes the number of times that media outlet j has reported on the event i in past reports. We have shown a direction to use GC, CTGAN, and TABGAN on top of the real MOX2-5 datasets to do a comparative analysis and show that MLP model efficiency grows with the increasing volume of training data.

• Using the top keywords of the four topic groups, the longitudinal changes of these four groups were then analyzed.
• Female journalists contributed more than might be expected to Danish media articles on parental leave.
• The orthographic lexicon can be accessed via letters directly or via a sublexical route where phonology is imputed by a simple associative network that can activate the phonological lexicon and then semantics.
• Hence, the algorithm learning process is to estimate the latent variables z, θ and φ of the joint probability distribution according to the observed variable w.
• 4 and 5 show that GPT-3.5-turbo displays poor discrimination, and it judges most pairs as making sense (both high hit and false alarm rate).

For instance, ‘journal of pragmatics’ began to be indexed by Scopus in 1977 and was never discontinued until 2021. Thus, all ‘language and linguistics’-related articles of the journal from 2000 to 2021 were collected. Meanwhile, ‘computer assisted language learning’ journal has been indexed in Scopus since 1990. However, the journal was discontinued in 1997 and reentered into ChatGPT App the Scopus database in 2004. Thus, for ‘computer assisted language learning’ journal, a set of articles published between 2004 and 2021 was collected. Our research sheds light on the importance of incorporating diverse data sources in economic analysis and highlights the potential of text mining in providing valuable insights into consumer behavior and market trends.

However, with advancements in linguistic theory, machine learning, and NLP techniques, especially the availability of large-scale training corpora (Shao et al., 2012), SRL tools have developed rapidly to suit technical and operational requirements. Machine language and deep learning approaches to sentiment analysis require large training data sets. Commercial and publicly available tools often have big databases, but tend to be very generic, not specific to narrow industry domains. Sentiment analysis is analytical technique that uses statistics, natural language processing, and machine learning to determine the emotional meaning of communications. However, P-RSF scores from the AT surpassed those from the nAT in classifying patients with and without MCI, with above-chance accuracy (69.5%) and a solid AUC value (0.82).

This paper presents a semantic analysis-driven customer requirements mining method for product conceptual design based on deep transfer learning and ILDA. Firstly, an analogy-inspired VPA experiment providing cross-domain stimuli is conducted to obtain feasible and innovative customer requirement descriptions of elevator. Secondly, a BERT deep transfer model is constructed to realize the customer requirements classification among functional domain, behavioral domain and structural domain in terms of the customer requirement descriptions of elevator. Last but not least, the ILDA is proposed to mine the functional customer requirements representing customer intention maximally. Hence, this paper provides a novel research perspective on feasible and innovative customer requirements mining in the product conceptual design through natural language processing algorithm. The ILDA method is applied to acquire the functional requirements topic-word distribution representing customer intention maximally.

As previously stated, the form converter can derive an instrument from an ontology. Similarly, this service enables the creation of an ontology based on an instrument. The D2R is a tool that converts relational content into semantic formats, allowing a quick conversion between these formats by automatically creating ontologies based on the schema of the content. The solution offers a service that provides practical tools to enhance the use of ontologies in the system and allow the continuous integration of different data sources, adapt to the evolution of ontologies, ensure availability, and avoid data loss. Once the data is in the REDCap database, changes in records are monitored through the Data Entry Trigger module, which can detect any changes. When it occurs, the Processor exports the edited data from REDCap and logs it into the relational database.

There are also general-purpose analytics tools, he says, that have sentiment analysis, such as IBM Watson Discovery and Micro Focus IDOL. One of the most prominent examples of sentiment analysis on the Web today is the Hedonometer, a project of the University of Vermont’s Computational Story Lab. First of all, it’s important to consider first what a matrix actually is and what it can be thought of — a transformation of vector space.

Through these reminders, the system helps researchers keep participants’ data up-to-date according to the formal protocol, avoiding critical protocol violations. The notifications may be sent by email or SMS to the recipients’ lists stored as metadata. Meaning can be imparted to data by using ontologies or other semantic standards, i.e., well-defined vocabularies that allow a precise and machine-readable description of domain-specific knowledge15. It may enable semantic interoperability, allowing systems to interpret the data in accordance with its formal definition16. In this sense, data can be shared accurately and reliably to enhance communication among computerized systems.

The most prominent source of error for the tool currently is the way it handles unlearned tissue types, such as lymph nodes, pancreatic islets, the desmoplastic stroma, and the occasional presence of neighboring gastrointestinal tissue. Lymph nodes and gastrointestinal tissue are highly irregular compared to the pancreatic features that were present in the training data, leading to completely arbitrary labeling of the unrecognized tissue areas. To overcome semantics analysis this, these regions can simply be cropped prior to analysis, as performed for our analyses. Islets comprise a small fraction of the pancreatic tissue area, and were labeled by the model as “other” (i.e. neither normal, ADM, or dysplasia), and therefore introduced only minor errors. In addition, the desmoplastic stroma is a prominent and histologically distinct feature of pancreatic disease that is currently unlearned and labeled as “other” tissue.

When there is pre-existing knowledge about both items in a pair, as was the case in our study, the cue representation instead changes asymmetrically to become more predictive of the upcoming target55,57. To evaluate our first question, we systematically manipulated semantic relatedness between the cue and target with a to-be-learned pair of words and compared accuracy between tested and restudied pairs after approximately 24 h. We found that although relatedness increases overall performance, it decreases the magnitude of the testing effect by substantially improving performance for restudied pairs, such that the relative additional benefit conferred by testing is less than for unrelated pairs.