Bibliometric analysis of Asian language and linguistics research: A case of 13 countries Humanities and Social Sciences Communications

Once events are selected, the media must consider how to organize and write their news articles. At that time, the choice of tone, framing, and word is highly subjective and can introduce bias. Specifically, the words used by the authors to refer to different entities may not be neutral but instead imply various associations and value judgments (Puglisi and Snyder Jr, 2015b). 1, the same topic can be expressed in entirely different ways, depending on a media outlet’s standpointFootnote 2. For example, certain “right-wing” media outlets tend to support legal abortion, while some “left-wing” ones oppose it. The test is based on a basic human psycholinguistic ability to understand combinations of two words, which has been of great interest to psychologists, linguists, and neuroscientists for decades17,18,19,20.

Nominalization refers to the downgrading of the rank from verbs (verbal groups) that serves the six processes to nouns (nominal groups). In this section, we will discuss the nominalization of verbs and verbal phrases serving all process types, which function as participants and circumstances in a clause after the change. This empirical research was conducted through a case study of the Governance volumes. First, the transitivity patterns of the ST and TT, and major tendencies of transitivity shifts were described through a comparative analysis, using a mixed approach of quantitative and qualitative analysis of the research materials. Thereafter, factors likely to motivate the tendencies were discussed with various translation examples. Two problems were noticed in our review of previous studies on the translation of Xi’s works of Governance.

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

We found that for related pairs, learning-induced greater representational change in the semantic structure of cue words than target words, while there was no statistically significant difference in the change in unrelated cue and target words. We then adapted an approach from neuroimaging literature for investigating asymmetrical representational change41,55. If the correlation between pre-learning cues with post-learning is less than the correlation between post-learning cues with pre-learning targets, this implies that learning draws cues towards targets in semantic space. This was indeed the pattern we observed for related word pairs (although as an isolated effect, the negative asymmetry value narrowly failed to survive corrections for multiple comparisons; however, the change in asymmetry relative to unrelated pairs was significant).

In terms of transitivity translation shifts and equivalence, Matthiessen (2001, p. 78–79), suggested that “equivalence is a matter of degree”, and “the degree to which two expressions in two different languages are equivalent will depend on how many features they share”. The study of reproducing ACPP in Governance has substantial empirical and practical value. The major difference between literary and non-literary texts “is that the first comprises the world of the mind and the imagination; the second, the world of reality, of facts and events” (Newmark, 2004, p. 10). Such a difference makes us wonder how to translate the meaning behind “the world of mind and the imagination” when they are parts of the texts comprising the meaning behind “the world of reality”. For this, we can explore the reproduction of the experiential meaning of ACPP in political texts.

Covarying collexeme analysis of the NP de VP construction

The similarity value is the dot product of X and Y divided by the squared magnitude of X and Y minus the dot product. The average nearest neighbor is calculated as the observed average distance divided by the expected average distance21. Where Γt denotes the microstate template assigned to the EEG signal at time t, and ||a|| denotes the Euclidean norm of the vector a. Therefore, in this paper, the quality of two sequences were evaluated in terms of spatial correlation, data explanatory, residual and dispersion. The ratio of the quality parameters between the two sequences was extracted as microstate quality feature.

In both cases, the encodings of the [CLS] tokens for all the news articles in a week were averaged to obtain a vector summarizing the information for that week. The first (referred to as BERT-truncated) considered only the first 30% of the tokens resulting from the tokenization procedure of the input news article. We truncated or padded the token vector with ChatGPT App zeros to get 510 elements and added the classification [CLS] and separation [SEP] tags. The resulting vector was fed into a pre-trained BERT encoder, which computed a 768-element encoding vector for each token. Among these, we only considered the encoding of the [CLS] token to represent the news article, as it captures BERT’s understanding at the news level.

• It has been discussed no less frequently than other Chinese expressions such as taishang zuo zhe zhuxituan ‘on the platform sits the presidium’ and wangmian qisui si le fuqin ‘Wang Mian’s father died when he was seven’.
• A Schematic of representational similarity matrices (RSMs) derived from the Similarity-Based Word Arrangement Task (SWAT) procedure before learning (purple RSM) and after learning (orange RSM); note that our real matrices would be 60×60 words rather than the 5 × 5 words used in this toy example.
• Differently, “ObS” (Objective Support) shares the strongest positive association with SFM and “UOS” (Use of Support) and “SbS” (Subjective Support) are the second and third, respectively.
• The platform allows Uber to streamline and optimize the map data triggering the ticket.
• Therefore, we excluded the FT dataset to determine MLP classification model’s (see Fig. 11) performance in comparison with the real dataset (see Tables 7, 8, 9 and 10).

Initially, we recruited twenty-five participants (19 men and 6 women) aged between 18 and 64; however, nine participants dropped in the middle of data collection due to medical reasons (e.g., pregnancy), lack of self-motivation, and device incompatibility issues. Therefore, the final data acquisition was performed with sixteen volunteering healthy individuals (12 men and 4 women) from Grimstad, Norway, for a period of 30–45 days (about 1 and a half months). This result suggested that the dual-template microstate analysis method has excellent generalizability across subjects. The method does not depend on specific individuals or subgroups, but has broader applicability. This has important implications for practical clinical applications, research and diagnosis of SCZ. In addition, the results also emphasize the importance of resting-state microstates as potential biomarkers or shared features of SCZ.

How did the Danish media report on the parental leave reform earmarking additional leave for fathers that came into effect in August 2022? Our sentiment analyses showed that articles about the reform were written with slightly more emotionality than a sample of articles from the “General News” control data, further supported by additional matched control article analyses. We found no differences in article sentiment between left-oriented and right-oriented newspapers, no difference across articles by male and female journalist and no interaction between political orientation and journalist gender. We conclude that articles about the reform were more emotional than we might expect, and this is the case across newspapers of different political orientations and for both male and female journalists.

New Faculty Reshape the Meaning of Inclusive Research, Teaching and Service

While explicit customer requirements tend to be self-evident as long as we can obtain raw data like questionnaire and interview, latent customer requirements are usually hidden in the semantics of customer requirements information and customers may not be distinctly aware of them. Analogical reasoning in the product conceptual design is the process of solving current design problems based on the solutions of past design problems5. Design process can be supported using analogical stimuli by assisting participants to overcome fixation and generate abundant solutions with more positive characteristics during ideation.

First, though the past few years have witnessed an increasing number of studies involving the translation of Xi’s books, few are dedicated to the Chinse–English translation of ACPP quoted in them. Much of the research explores the translation strategies and skills of the volumes or their specific linguistic features, including the translation of Chinese-specific expressions, or Chinese culture-loaded words, though involving the ACPP. Second, although various theories, the –additional translation theories or the modern ones–were adopted from different research perspectives, SFL was barely employed by them in translating Chinese-specific expressions or the semantics analysis ACPP in Xi’s works, thus offering avenues for this study to explore. Both NPs and VPs with significant attraction to the construction are the conventionality of frequently using these lexical items. The third typical meaning pattern of the construction under consideration pertains to the pairing of lexical items that denote the sense of “results” in the NP slot and those that denote the sense of “achievement” in the VP slot. Considering the instantiated clause in (1), shixian ‘realize’ in the VP slot significantly covaries with mubiao ‘targets’ in the NP slot of the NP de VP construction, and therefore the third typical meaning pattern is formulated.

For each semantic role, a textual entailment analysis is then conducted to estimate and compare the average informational richness and explicitness in each corpus. Based on the results of textual entailment analysis, the study further investigates translation universals at the semantic level and collects evidence for the influence of the translation process on informational explicitness as well as the semantic structure. But it can pay off for companies that have very specific requirements that aren’t met by existing platforms. In those cases, companies typically brew their own tools starting with open source libraries. The basic level of sentiment analysis involves either statistics or machine learning based on supervised or semi-supervised learning algorithms. With semi-supervised learning, there’s a combination of automated learning and periodic checks to make sure the algorithm is getting things right.

From the micro perspective, our goal is to quantify the bias of each media outlet in wording and sentence construction when composing news articles about the selected events. The experimental results align well with our existing knowledge and relevant statistical data, indicating the effectiveness of embedding methods in capturing the characteristics of media bias. First, we train embedding models using real-world data to capture and encode media bias.

Exploratory correlation analyses

A much larger dataset would be required to effectively leverage the high dimensionality of BERT encodings and model the complex dependencies between news and CCI indexes. Interestingly, the BERT-chunk model performed approximately the same as the BERT-truncated one. This is in line with the idea that most of the relevant information of a news article is contained at its beginning or that online readers focus mainly on the headline and the lead67. This study contributes to the discussion on online media’s role in shaping consumer confidence. By providing an alternative method based on semantic network analysis, we investigate the antecedents of consumer confidence in terms of current and future economic expectations. Our approach is not intended to replace the information obtained from traditional tools but rather to supplement them.

The noise covariance matrix was obtained by merging the matrices calculated from the baseline of all trials. We used source data to estimate the current density maps based on the minimum-norm estimate (MNE). These are the estimated time courses necessary for Granger causality analysis (see next section).

A “mountain stream” is a stream located on a mountain, but a “stream mountain” is not a thing at all. An “army knife” is not necessarily a knife located in the army but a type of knife useful in certain situations. TWT may exploit the fact that the text corpora that LLMs are trained on, no matter how large, almost entirely contain sensible text. Almost all text that people are exposed to is also sensible, but if the task requires, they are easily able to determine that certain word combinations don’t make much sense. Current LLMs may lack the depth of real-world knowledge that is required for this task. The frequency with which humans encounter phrases is a vital part of ‘meaningfulness’, as phrases that are encountered more often will naturally be accepted as meaningful (although low-frequency phrases can also be meaningful).

• Some examples include the ability to pass examinations required for advanced degrees, such as those in law2, business3, and medicine4.
• Using over 1.8 million online articles on Italian media covering four years, we calculate the semantic importance of specific economic-related keywords to see if words appearing in the articles could anticipate consumers’ judgments about the economic situation and the Consumer Confidence Index.
• The third meaning pattern is about the sense of “implementation”, which is typically realized by such verbs as zhiding ‘enact’, guanche ‘implement’, kaizhan ‘carry on’, qianshu ‘sign’, lvxing ‘perform’, and xingshi ‘perform’.
• The implementation process of customer requirements classification based on BERT deep transfer model is shown in Fig.
• There was also a weaker effect with the unrelated/nonword prime group, but this was not significant after correction for multiple comparisons.

There have been many efforts to recreate advanced staining images using more common input modalities15,16,17,18, and although they are useful for visualizing potential stain and intensity distributions, the algorithms are limited to predicting staining patterns of existing markers. If the user wants to analyze specific biological features for which there is no specific stain; however, ChatGPT simple stain translation will not suffice. The tool created here, however, can create objective binary interpretations of H&E images that segment histologic features of developing pancreatic cancer for which there is no reliable conventional immunostain. You can foun additiona information about ai customer service and artificial intelligence and NLP. These features, as previously described, are not easily distinguished by any other methods besides manual annotation.

In summary, the inconclusiveness of the current evidence indicates that there is a clear need to fill the gap between the hypotheses of language processing and the neuroscience behind it33. More recent theories have already tried to emphasize the grounding of abstract words in either the same sensory and motor systems as for concrete words34, in emotion35,36 or in social systems8. However, given the complexity and controversial nature of the issue as well as the theoretical shortcomings, our tools have been too limited conclusively to support or reject any theory of word processing. The above-mentioned controversy would suggest that there is a need for a clearer method to investigate the differences between the processing of concrete and abstract words.

Such being the case, measurement of explicitation merely at the syntactic level is not enough, and an investigation of it at the syntactic-semantic level is necessary. Therefore, it is of great importance to test whether universals like simplification and levelling out influence the semantic features and informational structure of translated texts. This can also enhance cross-linguistic translation comparative studies and contribute to our understanding of translation as a complex system (Han & Jiang, 2017; Sang, 2023). Large Language Models (LLMs) have revolutionized natural language processing, demonstrating remarkable abilities in understanding and generating human-like text. However, the way these models process and “understand” language is fundamentally different from human cognition.

As a result, this model’s predictions for surprisal will differ from those of a word-based surprisal model. Chomsky (1957) famously argued that linear models, were inadequate models of natural language, as they are incapable of capturing unbounded dependencies. To illustrate, consider the likelihood of the word “is” or “are” in context “The book/books that I was telling you about last week during our visit to the zoo”. Because the distance between this contextual word and the predicted verb can grow without bound, no specific value of n will yield an n-gram model that can correctly assign probability in such cases. Nevertheless, an exploration of the interaction between different semantic roles is important for understanding variations in semantic structure and the complexity of argument structures.

Priority is placed upon enforcing semantics in an absolute sense, where the meaning (or meanings) of a word remain relatively static within the context of the document, e.g. where bi-grams like heart attack should be correlated with myocardial infarction or coronary thrombosis21. Analysis on semantics, therefore, can be compared across the entire corpus despite similar concepts being represented by analogous phrases. If contextual information contained in tweets is to be relevant to emergency responders, two primary factors must be addressed. The first factor is that the semantic accuracy of any given system of analysis is relative to the topics trending at that point in time. The overall meaning of a given tweet is dependent on how the words it contains are used under immediate circumstances.

Although H&E samples were stained by the same Histopathology Core, it is likely that staining was done by different operators and used different machines. Following model development, generalizability and robustness to H&E staining differences were tested using synthetically altered H&E stains to show model consistency (Supplemental Fig. 5). Synthetic HE stains were created by randomly shifting the R, G, and B channels by up to ± 25% and applying Gaussian noise. Not only can these models replicate immunostaining data, they can extract more information than can be gained via immunostaining.