The Two Word Test as a semantic benchmark for large language models Scientific Reports

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Forecasting consumer confidence through semantic network analysis of online news Scientific Reports

Additionally, the success of clinical research directly depends on the correct definition of the research protocol, the data collection strategy, and the data management plan2. These elements drive the quality and reliability of the collected data that will be used to analyze the outcomes of a given study. Multivariate Granger causality of simulated data plotted in terms of partial directed coherence (PDC) (cf. color scale) in the time-frequency domain. The model predicted histologic features match what in expected in both normal and pancreatitis samples. (a) Predicted images show that tissue is dominated by normal acinar with pockets of clear ADM localization.

In line with the theory, a meta-analysis conducted on 45 published papers also found that people with a higher level of meaning in life tend to experience more subjective well-being (Jin et al., 2016). Due to the “black box” nature of LLMs tested here, it is unclear exactly why the LLMs provide meaningfulness judgments that are so different from humans. In the code provided, we have calculated similarity metrics for each phrase (cosine similarity of the two words in each phrase) based on some popular word embedding models such as word2vec. Future research could seek to find patterns using these metrics (or any number of other psycholinguistic properties) which may shed light on the instances in which LLMs are failing. Due to token restrictions, the phrases were randomly assigned to 8 subsets to ensure that the LLMs’ errors were not due to memory limitations. To prompt the LLMs’ judgment, we submitted the same instructions and examples originally provided by Graves et al.

Because the range of bias values differs across each topic, the color bar of different topics can also vary. Specifically, the square located in row i and column j represents the ChatGPT bias of media j when reporting on target i. We first analyzed media bias from the aspect of event selection to study which topics a media outlet tends to focus on or ignore.

Change in pairwise representational similarity

The graph indicates a negative correlation between change rate and borrowability, which is stronger in the global sample (WOLD) than in our own data (DiACL). Summary of main correlations between semantic change rates and cultural features/ semantic properties. You can foun additiona information about ai customer service and artificial intelligence and NLP. Process refers to a hypothesized process at hidden stages giving rise to the attested meanings at attested stages. Standard reinforcement learning algorithms typically learn by exploration, where the agent attempts different actions in an environment and builds preferences based on the rewards received.

In this case, demographic and vaccination information were integrated and compared to keep the data up-to-date and increase the completeness of the research dataset. Semantic annotation can underpin the exchange, use, and integration of data from different sources thanks to the aggregation of meaning in raw data. In other words, data becomes machine understandable and can be interpreted by distinct systems. Benefits are added for both EDC systems, and the user/researcher can take advantage of the best of each system. In this sense, the negative aspects of one can be mitigated by the positive characteristics of the other.

After the process was completed, the data consisted of 21,874 meaning tokens and 6,224 meaning types, distributed as polysemous meanings of 16,679 lexemes, compiled from the original list of 104 concepts (Table 1). This data formed the basis for the coding of semantic relations, described in next paragraph. In cases where consistent semantic interpretation over a large number of documents is important, methods have been employed to increase the immutability of the vocabulary. In Pedersen et al. one such mechanism is to reduce the vocabulary, while minimizing the reduction’s impact on meaning21. This has been accomplished by swapping words within an acceptable range based upon semantic similarity21.

Varying demands for cognitive control reveals shared neural processes supporting semantic and episodic memory retrieval

This article explores the concept of distributional semantics in LLMs and how it differs from traditional linguistic and philosophical notions of semantics. Finally, in this study we have limited ourselves to sources localized on the cortical surface even though many subcortical structures such as the thalamus and some parts of the basal ganglia are suggested to contribute to language processing108,109. Despite the fact that it is still unclear how activity from deeper structures can be detected by means of EEG source reconstruction, more studies are now claiming that activity from subcortical structures can reliably be estimated using high-density EEG110,111.

To carry out this study, we amassed an extensive dataset, comprising over 8 million event records and 1.2 million news articles from a diverse range of media outlets (see details of the data collection process in Methods). Our research delves into media bias from two distinct yet highly pertinent perspectives. From the macro perspective, we aim to uncover the event selection bias of each media outlet, i.e., which types of events a media outlet tends to report on.

Thus, our approach may prove more sensitive to discriminate between phenotypes in probabilistic subject-level terms than at the group level. Previous discourse-level evidence indicates that action-concept measures can discriminate between PD patients on and off medication8. Though inconclusive, our study suggests that examinations of this domain may also be worth pursuing to discriminate between patients with different cognitive profiles.

• As already mentioned, despite the difference in precision between larger and smaller samples, the results obtained through the application of the CSUQ are valid for small samples of usability and satisfaction tests.
• This brought us to 2,941 pairs of senses, each of which contains a source sense and a target sense annotated in English, as well as a number of realizations and a list of languages in which the shift was attested.
• Tweets can contain any manner of content, be it observations of weather related phenomena, commentary on sports events, or social discussion.
• Future research could seek to find patterns using these metrics (or any number of other psycholinguistic properties) which may shed light on the instances in which LLMs are failing.
• Nominalization refers to the downgrading of the rank from verbs (verbal groups) that serves the six processes to nouns (nominal groups).

In order to ensure a maximal utility for analogical stimuli, near-domain stimuli are provided to guarantee the feasibility and usefulness of the customer requirements and far-domain stimuli are selected to assure the novelty of the customer requirements. Besides, the collected customer requirements should be carefully evaluated and filtered by the domain experts. As for the Chinese transitivity system, despite their structural differences, Chinese and English share numerous commonalities in transitivity, particularly in processes, due to the similarity of human experiences, leading to similar sentence patterns and process types (Zhao, 2006). Processes barely change despite cultural differences, for it is the participant and circumstance components that mainly load the sociocultural elements. Several other core studies (e.g., Halliday and Matthiessen, 1999; Li, 2004a,b; Halliday and Webster, 2005; Peng, 2011) also proved that the transitivity systems of Chinese and English are proximate, despite superficial differences in the linguistic strata.

Therefore, in the media embedding space, media outlets that often select and report on the same events will be close to each other due to similar distributions of the selected events. If a media outlet shows significant differences in such a distribution compared to other media outlets, we can conclude that it is biased in event selection. Inspired by this, we conduct clustering on the media embeddings to study how different media outlets differ in the distribution of selected events, i.e., the so-called event selection bias. Microstate sequences are widely employed in the investigation of SCZ due to their rich pathological and semantic information (Lehmann et al., 2005). Research indicates that different psychological states and thought categories may have underlying correlations with different microstate topologies.

In such a scenario, Europeans’ expectations of Ukraine winning rise by, on average, 12 percentage points. However, even in such circumstances a settlement is still seen as the most likely outcome in 11 out of 15 countries polled. However, opinion in all European countries surveyed is strongly sceptical of Kyiv’s ability to win the war. In contrast to public opinion in Ukraine, only a small number elsewhere think a Ukrainian victory is the most likely outcome. The prevailing view in most countries (except for Estonia) is that the conflict will conclude with a compromise settlement. So, when it comes to the war’s end, European publics express the pessimism of the intellect while Ukrainians represent the optimism of political will.

In order to use the leading information coming from ERKs, we transformed the monthly time series into weekly data points using a temporal disaggregation approach56. The primary objective of temporal disaggregation is to obtain high-frequency estimates under the restriction of the low-frequency data, which exhibit long-term movements of the series. Given that the Consumer Confidence surveys are conducted within the initial 15 days of each month, we conducted a temporal disaggregation to ensure that the initial values of the weekly series were in line with the monthly series. GC conceived the method and model, did statistical analyses based on the results from the Markov model estimates, and wrote the text. The model has several shortcomings, most importantly that it cannot identify meaning change diachronically.

We recognize the need for semantic enrichment of data to exploit the full potential of activity sensor observations. This semantic representation enables data interoperability, knowledge sharing, and advanced analytics. The Semantic Sensor Network (SSN) ontology represents sensor-related information (such as data repositories, processing services, and metadata) and observations and is therefore valuable in environments where sensor data and observations play an important role. SSN leverages Semantic Web technologies and ontologies to provide a standardized and machine-understandable way to describe, discover, and reason about sensors and sensor data. SSN is an important component of the Internet of Things (IoT) and the broader Semantic Web concept.

By analyzing the occurrence of these subsequence patterns in microstates, clinicians may be able to diagnose SCZ patients with greater accuracy. The self-acceptance questionnaire (SAQ) was developed by Cong and Gao (1999) to measure the level of participants’ self-acceptance. The SAQ contains 16 items which can be divided into two subscales, namely self-acceptance and self-judge. All the items are rated on a 4-point Likert scale, ranging from 1 (strongly disagree) to 4 (strongly agree) for the items of self-judge and a reverse scoring for items of self-acceptance.

We developed an automated framework to capture semantic markers of PD and its cognitive phenotypes through AT and nAT retelling. The weight of action and non-action concepts in each retold story was quantified with our P-RSF metric, compared between groups through ANCOVAs, and used to classify between patients and HCs via machine learning. P-RSF scores from AT (but not nAT) retelling robustly discriminated between PD patients and HCs. Subgroup analyses replicated this pattern in PD-nMCI patients but not in PD-MCI patients, who exhibited reduced P-RSF scores for both AT and nAT retellings. Also, though not systematic, discrimination between PD-nMCI and PD-MCI was better when derived from AT than nAT retellings. Moreover, our approach outperformed classifiers based on corpus-derived word embeddings.

The flow network is a directed graph that can depict the relation between a certain variable and other multiple variables. In the flow network, each edge represents a pathway through which quantities can move from a source node (e.g., factors of social support) to a sink node (e.g., POM and SFM). Specifically speaking, in the current study, the GGM was used for the estimation and the EBIC as well as the LASSO were utilized for simplification and regularization (Epskamp and Fried, 2018; Epskamp et al., 2018a,b).

• The earliest stages of oncogene-induced pre-cancer evolution are marked by an expansion of ductal cells or by the conversion of the acinar cells to a ductal phenotype in an adaptive process known as acinar-to ductal metaplasia (ADM)13.
• In each test of the leave-one-out method, one patient’s data and one normal person’s data were selected as the test set, while the remaining 26 subjects’ data were used as the training set, and this process was repeated 14 times.
• Data are collected during the interviewer’s interaction with the research participant through the form available in the KoBoToolbox system.
• Therefore, when we compare the ST and TT and try to locate changes with the analytical unit of the transitivity system being the clause rank, rank shifts should be more visible than other types of participant and circumstance shifts.
• The following formulae were used to derive a scalar score for the tweet from an amalgamation of the component term vectors.

Instances of the NP de VP construction are ranked according to their association strengths between lexical items and the construction, which are presented in the first column; column name ‘NP de VP’ profiles typical instances of this construction; column name ‘obs. Freq.’ stands for observed frequencies of the construction in the corpus; column name ‘exp. Freq.’ means the expected frequency that a certain instance of the construction should occur in the corpus; column name ‘relation’ demonstrates whether a certain instance is attracted or repelled to the NP de VP construction; column name ‘Coll.s’ shows values of the association strength. The covarying collexeme analysis identifies “the association strength between pairs of lexical items occurring in two different slots of the same construction” (Stefanowitsch and Gries, 2005, p. 9) or investigates which lexical items in one slot covary with those in another slot. Specifically, this determines which potential lexical items in slot 2 cooccur with each potential lexical item occurring in slot 1 significantly more often than expected or vice versa. Its operationalization could be illustrated by the NP de VP construction demonstrated in the contingency Table 1.

ThoughtSource: A central hub for large language model reasoning data

Our method would thus enable us to address the issue of defining the spatio-temporal pattern without limiting ourselves to a prior definition of the ROIs. In the traditional definition of GC, connectivity is defined in the time domain ChatGPT App and only between two variables. However, the PDC method accounts for multiple brain areas (i.e. multivariate case)51, meaning that we can satisfy the requirement of GC to take all ROIs affecting the system into account49.

Captured time-series activity data are continuous in nature; however, we converted it into discrete tabular form for such classification problem after removing the “Timestamp” feature. Both the final and processed tabular data and its synthetic versions are part of the dataset. Regarding the choice of MOX2-5 medical grade sensor for physical activity data collection, it has been essential to underscore the distinctive advantages offered by these sensors in the context of this study. MOX2-5 sensors provide medical-grade precision in capturing physiological parameters during physical activity, enabling a nuanced analysis of participants’ responses. While alternative sensors such as accelerometers, video cameras, and gas chemical sensors are indeed valuable in specific applications, the MOX2-5 sensors specifically excel in offering real-time, high-fidelity data on physical activity changes. This level of granularity is crucial for understanding the intricacies of physiological responses during diverse physical activities.

In 2021, it acquired World Programming, a UK company that developed a compiler and runtime for SAS code. Altair started out in the mid-1980s as with the creation of HyperWorks, a CAE tools that was widely adopted by the auto industry and other manufacturers. The Troy, Michigan-based company expanded its product set by acquiring other HPC modeling and simulation tools, which today are sold through its HPCWorks unit. Over the years, the Anzo graph database was adopted by a number of organizations across financial services, government, healthcare, life sciences, and manufacturing, including Merck, Lilly, Novartis, Credit Suite, Bosch, and the FDA, according to its website. In 2016, the company acquired SPARQL City, which developed an in-memory graph query engine.

Levelling out, as one of the sub-hypotheses of translation universals, is defined as the inclination of translations to “gravitate towards the center of a continuum” (Baker, 1996). It is also called “convergence” by Laviosa (2002) to suggest “the relatively higher level of homogeneity of translated texts”. Under the premise that the two corpora are comparable, the more centralized distribution of translated texts indicates that semantic subsumption features of CT are relatively more consistent than the higher variability of CO.

In our pervious study9, we compared the predictive performances of our designed and developed MLP model with other state-of-the-art timeseries classification models, such as Rocket, MiniRocket, and MiniRocketVoting and our MLP model outperformed other classifiers on real. Furthermore, we have extended the study with a comparative predictive analysis on synthetic datasets. Therefore, in Tables 10, 11, 12 and 13, we have captured the results of these classifiers on different datasets to compare the performances.

A representative sample of protrudin-depleted cells imaged by SIM for the comparison of ER phenotypes. Sequential images (43.5 s at 1.5 s per frame) demonstrated compromised reshaping and connectivity defects in a cell sample treated with siRNA to deplete protrudin (Fig. 6a). A representative sample of cells treated with U18666A and imaged by SIM for the comparison of ER phenotypes. Sequential images (43.5 s at 1.5 s per frame) demonstrated reduced tubular network and fragmented ER structure in a cell sample treated with U18666A (Fig. 6a). A representative sample of cells treated with SKF96365 and imaged by SIM for the comparison of ER phenotypes. Sequential images (43.5 s at 1.5 s per frame) demonstrated that the ER was largely fragmented and featured as a disassortative network in cell samples treated with SKF9635 (Fig. 6a).

As we enter the era of ‘data explosion,’ it is vital for organizations to optimize this excess yet valuable data and derive valuable insights to drive their business goals. Semantic analysis allows organizations to interpret the meaning of the text and extract critical information from unstructured data. Semantic-enhanced machine learning tools are vital natural language processing components that boost decision-making and improve the overall customer experience. To supplement our analyses relating representational change and semantic structure to final recall success, we ran a generalized LMM with a logit link function (i.e. a mixed-effects logistic regression) using the glmer function from the lme4 package79. This model was fit using maximum likelihood estimation and a BOBYQA optimizer with a maximum of 200,000 iterations. As in our previous LMMs, the effect of subject identity was included as a random effect, and random effects of relatedness and learning condition were independently tested as potential random effects using likelihood ratio tests.

Indeed, their processing hinges on motor brain networks2,3,4 and is influenced by the speed and precision of bodily actions5,6. Since PD compromises these neural circuits and behavioral dimensions, action concepts have been proposed as a robust target to identify patients and differentiate between phenotypes1. However, most evidence comes from burdensome, examiner-dependent, non-ecological tasks, limiting the framework’s sensitivity, scalability, and clinical utility1,7,8,9. To overcome such caveats, this machine learning study leverages automated semantic analysis of action and non-action stories by healthy controls (HCs) and early PD patients, including subgroups with and without mild cognitive impairment (PD-MCI, PD-nMCI). The results of the P2 are interesting as effects of semantic priming on the P2 are rare with adults.

It results in the following data in Supplementary Material-4 (synthetic_data_GC_labelled.csv, synthetic_data_CTGAN_labelled.csv, and synthetic_data_TBGAN_labelled.csv) of total 88 KB in volume and they are used in this paper for experiments. The class distribution of FGC dataset, FC dataset, and FT dataset have been described in Tables 7, 8 and 9. Analyzing the complexity of the proposed ontology involves evaluating various aspects of the ontology’s structure, content, and reasoning requirements. Ontologies are suitable for knowledge representation, semantic search, data integration, reasoning, and applications where capturing the meaning and relationships between data entities is critical. They are commonly used in areas such as the Semantic Web, healthcare (for medical ontologies), and scientific research.

Semantic analysis techniques and tools allow automated text classification or tickets, freeing the concerned staff from mundane and repetitive tasks. In the larger context, this enables agents to focus on the prioritization of urgent matters and deal with them on an immediate semantics analysis basis. It also shortens response time considerably, which keeps customers satisfied and happy. Semantic analysis helps in processing customer queries and understanding their meaning, thereby allowing an organization to understand the customer’s inclination.