EEG analysis in patients with schizophrenia based on microstate semantic modeling method

It thus predicts there should be a difference between early processing when related and unrelated words are used as primes compared to when nonwords and unrelated words are used as primes. This is because both related and unrelated words should leave activity in the lexicons and are hence comparable. Alternatively, with nonwords and unrelated words, only the unrelated words should leave activity in the lexicons. Thus, the dynamics of priming across the two tasks will differ and this will reduce any correlations between them. According to the Triangle model, more early semantic priming with low frequency inconsistent than low frequency consistent target words should occur, and thus early interactions between word type (inconsistent and consistent) and prime type should be found.

Similarly, in this example, the expounding field of “功崇惟志, 业广惟勤” was translated as the enabling field by using the modal verb “must”. Here, “惟” refers to “lie in” and the original quote contains two circumstantial-attributive-relational clauses, literally meaning fruitful achievements lie in ambition and great business lies in diligence. However, they were translated into one possessive-attributive-relational clause and two material clauses in English.

The experiment starts after the subjects are fully aware of the experimental specifications, problems and procedures. The spoken data of thinking aloud is collected by video recording during the experiment, and a retrospective discussion is conducted before the end of the experiment so that some errors in the spoken data preprocessing can be avoided. Particularly, the thinking aloud of subjects and experiment materials are all in Chinese in order to reduce the cognitive load of subjects.

Also, mobile devices in digital data collection projects are frequently not owned by the people entering the data, which can be considered a risk to be managed46. On the other hand, due to the use of HTML5 features, KoBoToolbox provides a better user experience through modern form styles and a way to work offline, if needed, without using any additional applications, such as mobile apps. The second path (red round label with “2”) refers to the collection and continuous research data management process.

Method

This problem is also valid for the reconstruction of the change types (semantic relations, Section 2.3), which were coded between the concept meaning in relation to different meanings of a lexeme. Therefore, the probability to change was computed based on each meaning and each lexeme individually. Since the model cannot reconstruct which meaning turned into which meaning inside an etymon, the occurrence of semantic relations is an estimation based on the assumption that the concept meaning is primordial also in earlier states of the etymological tree. However, despite this shortcoming of the model, we believe that it will be possible to estimate the relative frequency of different change types pertaining to meaning relations by means of this model.

In other words, there is an additional force that drives the translated language away from both the source and target language systems, and this force could be pivotal in shaping translated language as “the third language” or “the third code”. In addition to a comprehensive analysis that includes all semantic roles, this study also focuses on several important roles to delve into the semantic discrepancies across the three text types. Considering the difference between Chinese and English semantic role tagsets, the current study chose some important and relatively frequent semantic roles as research focuses. The tagsets for both Chinese and English semantic role labelling of core arguments and semantic adjuncts are quite similar. Core arguments are labeled as ArgN or AN with N being numbers representing different types of relationships.

Table of contents

This is likely so because P-RSF uses LSA, yielding vectors based on our original texts’ bag of words. As such, our approach considers the frequency and co-occurrence patterns of the AT and the nAT, systematically designed to capture the action vs. non-action opposition while being controlled for over 20 psycholinguistic variables9. Conversely, GloVe embeddings result from a model trained with a large corpus that targets no specific hypothesis-driven semantic category. Thus, words’ semantic spaces are created by reference to multiple topics rather than predefined semantic fields informed by previous findings.

Faculty will benefit from career support such as navigating academic reviews and promotions, social engagement opportunities and introductions to community partners, as well as professional development retreats for research and teaching. Over the past three years, 20 faculty members have been hired at UC San Diego who are committed to advancing equity, diversity and inclusion using social justice frameworks. Each faculty member has a special focus on one of three key areas of impact, which include bridging Black studies and STEM, designing just futures for Indigenous, Black and migrant communities and expanding course offerings and curricular opportunities in Latinx and Chicanx Studies.

Using our latent components in our modelling task

This means that Indian, Israeli, and Turkish researchers collaborated relatively often with European researchers but less so with other countries in the other two sub-groups. As a different form of ‘degree’ weighted by the frequency of collaborations and signaling collaboration intensity (Ceballos et al., 2017), the ‘average weighted degree’ of this sub-group is 532.6. For the remainder of this study, by referring to Shen et al. (2018), a target article’s country was determined by the first author’s affiliation. When the first author’s affiliated country was not in Asia, the corresponding authors’ countries were counted. Given that the authorship role (i.e., corresponding authorship) was not provided by the Scopus API, this information was manually collected in Scopus.

Although semantic models worked well within BI tools, as more business functions came to be data-driven, different departments began to adopt different BI tools. A 2020 survey by 360Suite states that the average number of BI solutions used in an organization is 3.8, with 67% of respondents having access to more than one solution. The semantic layer does the heavy lifting of abstracting the complex underlying data into familiar business terms such as sales, revenue, customer, and product, establishing a common and standardized language across teams.

Computerized language analysis, thus, represents a promising tool towards richer clinical research on this population. C Statistical between-group comparisons were made via ANCOVAs, covarying for MoCA and IFS scores. D Classification analyses were based on support vector machines, with results represented via receiver operating characteristic (ROC) curves, confusion matrices, and distribution plots of P-RSF scores. These analyses were applied to discriminate between (i) all PD patients and all HCs, (ii) PD-nMCI patients and HCs, (iii) PD-MCI patients and HCs, and (iv) PD-nMCI and PD-MCI patients. However, all of these tasks are time-consuming because they demand the careful inspection of a significant amount of data. The REDCap software natively offers valuable tools to help data managers and researchers.

BERT Tone has also been used in Danish applied settings, for example, to classify the sentiment of student evaluations in accordance with the students’ numeric scoring of teaching37. Although we did find that news regarding the leave reform was less neutral than “General News” articles, the effect was relatively small. We interpret our small effect size, considering the values of neutrality and impartiality that are generally accepted in journalism38.

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

Concurrently, research on a country’s national language(s) and dialects were chiefly conducted. In particular, the popularity of a certain language as a research topic seems to reflect the speaker population of the language in a country. In Israeli research, ‘Russian’ was one of the top 20 keywords, and it was also one of the frequently co-appearing keywords alongside ‘Hebrew’, owing to the significant population of Russian immigrants in Israel (Lerner, 2011). For the target and citing articles, using Scopus API, each article’s detailed information was collected.

These included 32 verbs per text, chosen based on semantic, syntactic, and distributional criteria to operationalize the action/non-action distinction. Both texts communicated mostly literal meanings and contained no jargon (for full transcriptions and ChatGPT App English translations, see Supplementary Information 2). The quest for cognitive markers of Parkinson’s disease (PD) highlights the usefulness of assessing action concepts—mainly verbs denoting bodily movement, such as run, jump, applaud, and dance1.

In contrast, the analogy model performs well above chance and almost triples the accuracy of the similarity model by correctly identifying the target 38% of the time. This result suggests that semantic change relies on not just similarity between meaning, but recurring or regular meaning shifting strategies. For target inference, we embedded the senses in DatSemShift using contextual embeddings particularly phrase-BERT (Wang et al., 2021). This involved replacing obscure words and spellings with common ones and removing some punctuation. Phrase-BERT is an adapted version of the BERT model (Devlin et al., 2019) that embeds word and phrase meanings in a shared high-dimensional space, informed by context in natural language use.

However, five years is a widely-used citation window in several disciplines (Campanario, 2011; Mingers and Leydesdorff, 2015). Because it was impossible to collect the citing articles to the fullest level (i.e., up to five years) for those published since 2016Footnote 3, the current study collected the citations for only target articles published from 2000 to 2015. Once the target articles were collected, citing articles that referenced the target articles were collected using Scopus.

Our methodology incorporates algorithmic measures to systematically gather news data. Further, we have the negative correlation between borrowability and semantic change rates, established both through our own data (DiACL) as well as the World Loanword Database (WOLD) (Figure 8, see Section 3.3). If we follow a model where more frequent and salient words are less likely to be borrowed ChatGPT and more stable (i.e., the basic vocabulary-model), we would expect a reverse correlation, where more unstable lexemes are more likely to be borrowed and change their meaning. It is possible that an investigation of basic vocabulary would have given a different result. A possible scenario is that lexemes that are borrowed are more likely to become distinct and frozen in their meaning.

Significant main effects and interactions were probed using pairwise comparisons with Holm-Bonferroni corrections for multiple comparisons. In the final learning round, 30 of the pairs underwent retrieval practice (testing) and the other 30 were restudied. If they could not remember the target word, participants were encouraged to take a guess, or they could leave the box blank. Asking participants to type the paired words in the restudy condition, rather than having them make an additional relatedness judgement as in the first two learning rounds, allowed us to match the behavioral response with that of the testing condition (i.e. typing a word). This also served to reduce the differences between behavioral responses in the restudy condition and the final test, where all pairs would be probed by asking the participant to type a word. Another potential limitation could come from our admittedly restricted assay of semantic space.

8, among the top 30 keywords in Table 4, ‘culture’-related topics were also consistently popular throughout the past 22 years. ‘Attitude,’ ‘gender,’ ‘identity,’ ‘ideology,’ and ‘translation’ were frequently co-appearing keywords alongside ‘culture.’ Notably, ‘grammaticalization’ and ‘academic writing’ were also hot topics. For this analysis, the top 30 keywords for every three years (illustrated in Table 5) were expanded to the top 100 keywords for every three-year period.

• Core arguments are labeled as ArgN or AN with N being numbers representing different types of relationships.
• Showing that pairs are drawn together, however, does not show how they become more similar.
• Semantic analysis methods will provide companies the ability to understand the meaning of the text and achieve comprehension and communication levels that are at par with humans.
• Apart from a few attempts54,55, the spatio-temporal patterns of interactions between brain areas on word processing has yet to be defined.
• In this study, we used event-related-potentials (ERPs) to examine the results of two different types of semantic priming.

You can foun additiona information about ai customer service and artificial intelligence and NLP. We notice that there has been literature investigating the choice of events/topics and words/frames to measure media bias, such as partisan and ideological biases (Gentzkow et al. 2015; Puglisi and Snyder Jr, 2015b). However, our approach not only considers bias related to the selective reporting of events (using event embedding) but also studies biased wording in news texts (using word embedding). These two perspectives are distinct yet highly relevant, but previous studies often only consider one of them. For the choice of events/topics, our approach allows us to explore how they change over time.

REDCap is effective and efficient for managing data and conducting research after the initial collection phase, allowing researchers to have a more comprehensive view of the project database, including creating custom reports and accessing descriptive data analysis. KoBoToolbox allows for a more delightful collection through a clean, friendly, practical, and accessible interface for any device. Then, the REDbox framework fills the remaining gaps by offering extra functionality to enhance the researcher experience and underpin the research data lifecycle. The Alert System was designed to periodically send notifications to the research centers regarding not-answered queries and pending data collection based on the scheduled events of each study.

The P-RSF matrix was then estimated using the Hadamard product (i.e., element-wise product) between the occurrence matrix and the verb importance vector—the lower the P-RSF value, the lower the P-RSF value, the lower the weight of the target (action or non-action) concepts in a retelling. This matrix was used for inferential analyses (via ANCOVAs) and as a feature matrix for machine learning analyses. The relevance of this article lies in the innovative approach to supporting TB research.

Their training data was labeled as “election related” or “non election related” and focused on tweets that occurred during a parliamentary election in Venezuela in 2015. Their objective was to attempt to predict whether a tweet could be identified as election related based upon the vector representations of words contained in the tweet. We have restricted our current analysis of semantic change within individual words, and we acknowledge that this semasiological approach might not be fully representative of the onomasiological aspects of meaning semantics analysis change. In reality, meaning change results from a lexical competition process where words in the lexicon compete to express an emerging meaning (e.g., see work on chaining that formulates lexical competition as models of categorization as in Xu et al., 2016 and Habibi et al., 2020). Understanding these competing dynamics at the scale of the lexicon across languages may be challenging, since tracking the space of possible alternative lexical items can be infeasible due to its size but also the sparsity of crosslinguistic diachronic data.

These scores are the raw cosine similarity, and have not been min-maxed for their relative time delta. In these works, the authors aim to analyze and correlate social media data, specifically Twitter, to accommodate multiple uses. Different techniques are employed to widen the capabilities of analysis, but depend on significantly larger datasets. The aim of this paper is to increase the flexibility of the systems employed by deliberately reducing the amount of input data. The assertion here is that a reduction in data input increases the likelihood of the algorithm being able to interpret relevant meaning specific to the events as they occur.