REDbox: a comprehensive semantic framework for data collection and management in tuberculosis research Scientific Reports

However, none of these reports employed a paradigm strategically designed to elicit action concepts in unfolding speech, let alone while exploring their sensitivity to distinct disease phenotypes. A fruitful path for clinical PD research thus emerges at the crossing of behavioral neurology, cognitive neuroscience, and natural language processing. In our study, we investigated the spatio-temporal dynamics of visual word processing both in terms of neural activation, frequency and directionality of information flow. You can foun additiona information about ai customer service and artificial intelligence and NLP. In EEG connectivity studies, spurious connectivity can occur due to the spatial spread (resulting from volume conduction) during which signals coming from different neural sources are mixed before reaching the scalp surface. Thus, connectivity measured on this surface could reveal artificial or spurious connections which do not result from true neuronal interactions47.

Through a granular analysis of the dimensions of consumer confidence, we found that the extent to which the news impacts consumers’ economic perception changes if we consider people’s current versus prospective judgments. Our forecasting results demonstrate that the SBS indicator predicts most consumer perception categories more than the language sentiment expressed in the articles. ERKs seem to impact more the Personal climate, i.e., consumers’ perception of their current ability to save, purchase durable assets, and feel economically stable. In addition, we find a disconnect between the ERKs’ impact on the current and future assessments of the economy, which is aligned with other studies68,69. While the Consumer Confidence Index has often been considered a suitable predictor of economic growth and a good indicator of consumers’ optimism about the current economy, short-term estimations may show deviations from long-term trends, likely caused by nonsystematic shocks. First, we want to know whether there is variation in the evolutionary dynamics of different meanings.

This means the generation of phonology would be earlier than the P2 effect might suggest. The speed at which semantics is accessed by words with consistent (simple) and inconsistent (difficult) spelling–sound correspondences can be used to test predictions of models of reading aloud. Dual-route models that use a word-form lexicon predict consistent words may access semantics before inconsistent words. It predicts inconsistent words may access semantics before consistent words, at least for some readers. We tested this by examining event-related potentials in a semantic priming task using consistent and inconsistent target words with either unrelated/related or unrelated/nonword primes. The unrelated/related primes elicited an early effect of priming on the N1 with consistent words.

Data availibility

We thank the Fields Institute for financial support and facilitating the collaborative research project. We thank Gemma Boleda for discussion and feedback, Yiwei Luo and Aparna Balagopalan for sharing resources. Results of directionality inference from generalized linear mixed modeling with language as a random effect. These technologies not only help to optimise the email channel but also have applications in the entire digital communication such as content summarisation, smart database, etc. And most probably, more use cases will appear and reinvent the customer-bank relationship soon.

For example, if negative sentiment increases after a new product release, that could be an early indication that something is going wrong, enabling the company to do a deep dive to understand which features are causing problems or to get more agents on board to handle problems. TruncatedSVD will return it to as a numpy array of shape (num_documents, num_components), so we’ll turn it into a Pandas dataframe for ease of manipulation. Repeat the steps above for the test set as well, but only using transform, not fit_transform. The values in 𝚺 represent how much each latent concept explains the variance in our data. When these are multiplied by the u column vector for that latent concept, it will effectively weigh that vector.

If you’re not familiar with a confusion matrix, as a rule of thumb, we want to maximise the numbers down the diagonal and minimise them everywhere else. This article assumes some understanding of basic NLP preprocessing and of word vectorisation (specifically tf-idf vectorisation). Latent Semantic Analysis (LSA) is a popular, dimensionality-reduction techniques that follows the same method as Singular Value Decomposition. LSA ultimately reformulates text data in terms of r latent (i.e. hidden) features, where r is less than m, the number of terms in the data.

Our sequential Generator model had three Dense layers with two layers activated by the “ReLU” activation function. We maintained the dimensions of the output layer like the dimensions of the dataset. The first two layers were activated by the “ReLU” activation semantics analysis function, and the output layer was activated by the “Sigmoid” activation function to discriminate the real (True or 1) and synthetic (False or 0) data. We compiled the Discriminator model with optimizer as “ADAM” and loss function as “binary_crossentropy”.

There may be dual motivations for semantic change to take place in regular ways from the perspectives of speaker and listener. From a speaker’s view, regular semantic change might facilitate the grounding or structuring of new meaning given existing words (Srinivasan et al., 2019), and hence easing the process of creating and learning meaning change. From a listener’s view, regular meaning change might facilitate the interpretation or construal of novel meaning, provided the speaker and listener have some shared knowledge about the world and the situation (Clark and Clark, 1979; Traugott and Dasher, 2001). Importantly, we believe that regularity may be manifested and understood in different aspects in the context of semantic change across languages.

The value range of Lin Similarity is divided into 9 subintervals, and the number of texts in CT and CO that fall into each subinterval is counted. This figure provides a clearer illustration of the nuanced differences between the Lin Similarity distributions of CT and CO than a boxplot. The value range of Wu-Palmer Similarity is divided into 10 subintervals, and the number of texts in CT and CO that fall into each subinterval is counted. This figure provides a clearer illustration of the nuanced differences between the Wu-Palmer Similarity distributions of CT and CO than a boxplot. The other major effect lies in the conversion and addition of certain semantic roles for logical explicitation.

Bibliometric analysis of willingness to communicate in the English as a second language (ESL) context

At the lab, the owners were instructed to say words for objects before showing their dog either the correct item or a different one. For example, an owner might say “Look, here’s the ball”, but hold up a Frisbee instead. The experiments were repeated multiple times with matching and non-matching objects. More direct evidence for canine cognitive prowess came in 2011 when psychologists in South Carolina reported that after three years of intensive training, a border collie called Chaser had learned the names of more than 1,000 objects, including 800 cloth toys, 116 balls and 26 Frisbees. Dogs understand what certain words stand for, according to researchers who monitored the brain activity of willing pooches while they were shown balls, slippers, leashes and other highlights of the domestic canine world.

Colleges can employ support group intervention, which has positive impacts on social support among college students (Lamothe et al., 1995; Mattanah et al., 2012). As King and Hicks (2021) implied, self-acceptance may contribute to increasing meaning in life and some related literature are listed below. Longitudinal findings showed that college students who feel unoriented from their true selves, indicating a lower level of self-acceptance, tend ChatGPT App to be devoid of academic motivation, perceiving all efforts as meaningless and showing a low level of meaning in life (Kim et al., 2018). Moreover, self-acceptance was found to share a robust relation with increased positive feelings and life satisfaction, which is identified as the promoter of meaning in life (Miao and Gan, 2019; Liu F. et al., 2021). Indeed, it can be inferred that self-acceptance could be closely related to meaning in life.

A visual and intuitive form builder is available, or forms can be imported as XLS files. In the case of health research, semantic annotation can help describe the data that is being collected. It can be helpful to extract and link different research datasets described using the same vocabulary.

Correlations between tasks

That functional nature of activity remains unchanged in TT–hence the same field of exploring in TT. Here is a case of compressing types of process, but it involves only a shift within a single transitivity process, the relational one. We collected all the ACPP and their translations in the three Governance volumes as research materials, to address the research questions. The first volume (Xi, 2014a) explored President Xi’s critical works, including the speeches, talks, interviews, instructions, and correspondence from November 15, 2012, to June 13, 2014.

In the initial testing, each formula was executed in tandem, and the equations would be used to compare the effect of variation in the parameters. For purposes of consistency, and to distinguish from previous terminology, new symbols will be used for the components necessary for these comparisons. The symbol \(\alpha\) designates the initial search or seed term, the basis of all comparisons for these formulas. 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.

The TWT differs from these cases in that it does not directly require inference or reasoning. The reaction time of making meaningfulness judgments in tasks similar to TWT is around 1 s for humans19,32, which suggests that it is a qualitatively different process than those used in typical benchmark tasks such as puzzle solving or logical and commonsense reasoning. A limitation in breaking down a complex chain of reasoning into smaller problems should not affect performance on the TWT. Understanding these phrases requires understanding the constituent concepts, and then using world knowledge to determine whether the combination makes sense in some manner.

We found a robust difference in the semantics of how female journalists wrote about the reform, relative to male journalists, and that female journalists contributed to media coverage at a higher-than-expected rate. The tendency for media coverage to be written with a non-neutral sentiment can be understood in terms of the enduring political tensions over gender equality, the role of the EU and families’ rights to self-organization. That female journalists over-contributed to media coverage is interesting in understanding topic assignments or interest in parental leave.

Meaning patterns of the construction under investigation also accord with the theory of prototypicality. Prototypicality originally means the degree of category membership (Goldberg, 1995), and in this research, it means the identified meaning patterns are the most typical ones or the most representative of the meaning denoted by the NP de VP construction. Meaning patterns of the NP slot and the VP slot are confirmed by referring to the semantic features of these NPs and VPs. Those lexical items with statistically significant association strengths represent core members of the NP de VP construction. The meanings of these core members in turn represent the prototypical meaning of this construction. Core members of NPs in this construction briefly include zhidu “regulation”, tixi “system”, yewu “business”, etc., and thus the typical meaning patterns are “regulations”, “systems” and “business” (cf. Fig. 2).

Among these, explicitation stands out to be the most semantically salient hypothesis. It was first formulated by Blum-Kulka (1986) to suggest that translated texts have a higher level of cohesive explicitness. Baker (1996) broadened its definition into the “translator’s tendency to explicate information that is implicit in the source text”, emphasizing that explicitation in translated texts is not limited to cohesion, but can also be observed at the informational level.

It is not surprising that average consumers have a better understanding of their personal situation when responding to questions but may be less informed about economic cycles. When answering questions about their own financial situation, individuals are likely to have a more accurate understanding of their personal circumstances. However, when it comes to broader economic trends and cycles, the average consumer may not have the same level of knowledge or expertise. This is understandable, as economic cycles can be complex and difficult to understand without specialized training or experience.

According to the table, it can be observed that in the combination of template and data consistency, the GEV of SCZ patients reached 88.5%, while the GEV of HC was 85.5%. This indicates that the two microstate templates can effectively express the corresponding original brain topographic. However, in inconsistent combinations, the average GEV of the HS sequence was only 74.4%, while the average GEV of the SH sequence was 78.9%. This means that inconsistent combinations exhibit significantly lower quality while expressing the original data. Other quality evaluation indicators have also produced similar results, emphasizing that the combination of template and data consistency exhibits higher quality in constructing microstate sequences.

All three domains including tubules (cyan), sheets (yellow) and SBTs (magenta) were precisely classified by ERnet (Fig. 2d). A 3D rendering of ERnet segmented structures demonstrates attachment of tubules to sheets. A 3D reconstruction of SIM image sections validated that SBTs are directly attached to sheets and are not the result of a projection view artifact. All three domains including tubules (cyan), sheets (yellow) and SBTs (magenta) were precisely classified by ERnet (Fig. 2e).

In a similar vein, meanings of core members of VPs are summarized as such meaning patterns as “implementation”, “achievement”, and “establishment” (cf. Fig. 1). This paper proceeds further than Shen and Wang’s (2002) study in at least two respects. On the one hand, we considered not only lexical items that could enter the NP slot of the NP de VP construction but also the ones that are representative of the typical meanings of the NPs. By so doing, it will further facilitate our understanding of the typical meaning that the NP de VP construction could denote.

Finally, the User Support module is a supporting tool to facilitate communication between research teams (often located in distinct research centers) and the project’s coordination staff (Fig. 8). This tool allows users to send specific requests regarding the data stored in the REDCap database, such as unlocking records for editing and data deletions. The Processor receives the data collected in KoBoToolbox as a JSON object, parsed to remove unnecessary elements unrelated to the data of interest. After verifying the authentication credentials, the metadata is queried to obtain the URL and the token of the REDCap API (from redcap_projects) and to verify if it is the first form in the project (from redcap_forms).

In each case, we take the source probability ratio of a pair of senses in Equation (4) to be proportional to the ratio of their values under the predictor variables in question. One can train machines to make near-accurate ChatGPT predictions by providing text samples as input to semantically-enhanced ML algorithms. Machine learning-based semantic analysis involves sub-tasks such as relationship extraction and word sense disambiguation.

These citations constitute only a small part of the whole political work and some are only a part of a complex sentence; therefore, they often inevitably bear the transitivity characteristics of political texts, more or less. “尚贤者” is an embedded identifying-relational clause that functions as a participant in the whole sentence “尚贤者, 政之本也” (respecting the virtuous is the root of governance). In the English translation, “尚贤者” was reproduced as a nominal group “exaltation of the virtuous”, instead of rendering it as an embedded clause with a verbal group to be consistent with the original structure. The experiential meaning realized in two clauses in the ST now is condensed into one clause in English, stressing the most important process based on the translators’ understanding. The tendency of the compression of process types can change the intensiveness of meaning realized in clauses. As Chinese relies much on verbs and verbal groups to construe the experience of the world, clauses of different kinds tend to occur in which more information is embedded, causing the high intensity of meaning expressed in one sentence.

Change in overall representational similarity structure

We then normalized these shift values by dividing each by half of their range across all shifts. Since we discarded all shifts where either source or target did not have an available value for concreteness, frequency, or valence, we analyzed a reduced set of 859 semantic change pairs. Alternative methods of assigning values to senses that retain more data points can be found in Supplementary material, and our results hold robustly in that more exhaustive dataset. Earlier, tools such as Google translate were suitable for word-to-word translations. However, with the advancement of natural language processing and deep learning, translator tools can determine a user’s intent and the meaning of input words, sentences, and context. Albeit extensive studies of translation universals at lexical and grammatical levels, there has been scant research at the syntactic-semantic level.

The singular value not only weights the sum but orders it, since the values are arranged in descending order, so that the first singular value is always the highest one. The REDbox framework is constantly evolving to meet the target audience’s needs, taking into account the dynamism and multidisciplinarity of the health research area. As future work progresses and as the software matures, specific comments from key users will be collected to guide the evolution of each module. Although the TB scenario motivated the solution, it applies to other health fields as well. This work has presented REDbox, a comprehensive framework for integrated data collection and management in tuberculosis research. The use of REDCap and KoBoToolbox together has allowed the transparent combination of the advantages of each, helping researchers manage and maintain data while increasing the satisfaction of the final users responsible for collecting data in the field.

After manually processing those overlapping lexical items in the NP slot of the NP de VP construction, we finally confirmed 62 types of nouns. Considering their covarying collexemes in the VP slot of the construction, a 62 (types of nouns in the NP slot) × 99 (types of covarying collexemes in the VP slot) contingency table is subsequently formatted. Running the function hclust in R language yields a cluster dendrogram presented in Fig. This observation further supports our hypothesis that the trends in semantic change direction with regards to concreteness, frequency, and valence of the source and target words are shared and not language-specific. Semantic analysis is a method used in linguistics, computer science, and artificial intelligence to understand the meaning of words and sentences in context. It examines relationships among words and phrases to comprehend the ideas and concepts they convey.

Additionally, collaborative relationships or citation connections among the sampled countries might inform new narratives. Another future direction that would be vital to expanding our field would be considering why the research impact of these 13 countries differed and, furthermore, what determines their impact within ‘language and linguistics’ research. The last analyses about impactful topics have also shed light on another possible research direction. The results of Tables 5–7 substantiated that the research interest in computerized language analyses has intensified among the Asian ‘language and linguistics’ community. Conversely, the findings of ‘language and linguistics’ research are becoming critical ingredients in cutting-edge Computer Science technologies (Clark et al., 2012; Haddi et al., 2013; Rodriguez et al., 2012).

• The semantic role labelling tools used for Chinese and English texts are respectively, Language Technology Platform (N-LTP) (Che et al., 2021) and AllenNLP (Gardner et al., 2018).
• “Bringing Cambridge Semantics to Altair’s broad customer base through the Altair Units business model–and integrating it into Altair RapidMiner–is an exciting prospect for us and for our customers,” Pieper said in a press release.
• This leads to an idiosyncratic information structure in the target language and hence, the deviation between the translated and target languages.
• The model predicted histologic features match what in expected in both normal and pancreatitis samples.

In relation to lexical items in the NP slot of the construction, Shen and Wang (2000) argued that NPs that denote a sense of prominence (i.e., high informativity and/or high accessibility), could enter the NP slot of the construction. Their argument is further corroborated by the findings of this study which has identified such meaning patterns as “internal traits”, “medical names”, “regulations”, “results”, “systems”, and “business”. According to Shen and Wang (2002, p. 30), specific nouns are more accessible than abstract nouns.

Semantic analysis plays a vital role in the automated handling of customer grievances, managing customer support tickets, and dealing with chats and direct messages via chatbots or call bots, among other tasks. Moreover, granular insights derived from the text allow teams to identify the areas with loopholes and work on their improvement on priority. By using semantic analysis tools, concerned business stakeholders can improve decision-making and customer experience.