A deep semantic matching approach for identifying relevant messages for social media analysis Scientific Reports

Our state-of-the art approach allowed us to investigate patterns of information flow in the spatial, temporal as well as spectral domains. Our results showed a network of interactions involving regions located along both ventral and dorsal pathways where areas at both ends of the stream were mainly involved in sending information (right superior occipital lobe and orbitofrontal gyrus on both hemispheres). By contrast, areas which were largely recipients of information were the left anterior temporal lobe and right middle temporal gyrus. These differences all concerned information transfer routes flowing to the left anterior temporal lobe (ATL), suggesting that this area is largely involved in the processing of abstract words. Further exploratory analysis revealed generally higher connectivity during concrete word processing. These findings suggest that abstract and concrete words are processed in partially overlapping networks even though the strength of their connectivity exhibits different spectral and temporal properties.

Participation in this experiment took place over two days, with the sessions occurring no more than 28 h apart (see Fig. 1 for a schematic of the procedure). On Day 1, participants first performed a multidimensional similarity rating task using a drag-and-drop interface (Fig. 2A; similar to an approach from work in neuroimaging49, which used picture stimuli instead of words). Following this similarity-based word arrangement task (hereafter referred to as the SWAT), participants completed a learning task, where they were given two opportunities to initially learn a set of 60 words pairs (30 related; 30 unrelated). We note that in our pre-registration of this experiment we had stated that participants would only have one initial learning opportunity before the test/restudy manipulation; however, pilot data suggested that one learning opportunity was not enough to yield sufficient accuracy on Day 2. Then, participants were given a third opportunity to engage with each pair via either testing or restudying.

Diamond Dust: A Dream Solution to Climate Change

The first, Continuous Bag-of-Words (CBOW) trains the neural network by using the context words as the input and the expected target word as the output. The intended use here is to predict a single word based upon an input of one or more context words. While the data used in this study does not require privacy accommodations, other kinds of data might necessitate privacy-aware methods. For example, user location is collected on social networks through cell phones, wearable devices, etc. Qi et al.23 presented a point-of-interest category recommendation model that is privacy-aware. LSTM-based neural architectures are used for recommendations and users are classified into similar groups via hashing to protect user privacy.

A representative sample of cells treated with NaN3 and imaged by SIM for the comparison of ER phenotypes. Sequential images (43.5 s at 1.5 s per frame) demonstrated fragmentation of the ER tubular network, which resulted from the lack of ATP (Fig. 6a). A control (healthy) cell treated with the chemical vehicle DMSO, imaged by SIM for the comparison of ER phenotypes. A 3D projection view of a U2OS cell demonstrates that the tubular ER is largely flat and singly layered.

Behavioral representational similarity analysis reveals how episodic learning is influenced by and reshapes semantic memory

Let’s say that there are articles strongly belonging to each category, some that are in two and some that belong to all 3 categories. We could plot a table where each row is a different document (a news article) and each column is a different topic. In the cells we would have a different numbers that indicated how strongly that document belonged to the particular topic (see Figure 3). The AT and the nAT were created through a systematic protocol used in previous action semantics research4,7,9,21,48. The former story focused on the characters’ bodily movements, including single-limb and whole-body actions performed in isolation or during interactions with objects and other people (e.g., Johnny ran quickly to the place where the clown was jumping and dancing). This text offered several locative and temporal specifications, alongside details of how bodily actions were performed.

Furthermore, the process hampers their ability to explore the data themselves for deeper insights and gain topical intelligence from it that is immediately actionable. Second, there is the tendency for participant and circumstance rank shifts of nominalization. It means that verbs or verbal groups in a clause are changed to nouns or nominal groups that function as participants or circumstances. The fewer processes with more participants or circumstances in a clause, the denser the experiential meaning realized in one clause and the more emphasis on the meaning expressed in processes.

Besides, based on the word embedding model and the bias estimation method based on Semantic Differential, we can objectively judge each media’s attitude towards Russia and Ukraine (e.g., whether a media tends to use positive or negative words to describe either party). Once a news outlet is detected as apparently biased, we should read its articles more carefully to avoid being misled. 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). To determine the model classification efficiency, we trained a MLP classifier using the following data samples—R, FGC, FC, FGC + R, FC + R and GC + FC + R. In FGC + R, FC + R and GC + FC + R, we trained the MLP model with synthetic data and perform classification on the real data, following the transfer learning approach. The average classification outcomes of four executions have been captured in Tables 7, 8, 9 and 10.

• The hierarchical nestification structure is illustrated by the fact that one sub-structure functions as a semantic role (usually A1 or A2) in its dominative argument structure.
• For the randomly selected targets, both similarity and analogy models performed well above the chance level.
• If they could not remember the target word, participants were encouraged to take a guess, or they could leave the box blank.

You can foun additiona information about ai customer service and artificial intelligence and NLP. First, different sources of the instances that we counted upon prompt the findings that are not fully in agreement with each other. Zhan (1998) considered verbs from a Dictionary for Usages of Verbs (Meng, 1987) and exemplified his argument with invented examples, while this research considers examples from real data of BCC. It should be noted that I am not meant to downplay Zhan’s research without considering probabilities of instances with the construction at issue.

For example, to predict user created content, a view of tweets created by a particular user would be isolated, and the neural network trained on the user’s tweets as a single document. Figure 2 summarizes the results from directionality inference with aggregated data across languages. Overall, we observed that concreteness alone accounts for these data with 73.6% accuracy, which substantially exceeds the chance-level accuracy (50%) and the accuracy of the two alternative predictors (9.9% better than valence and 19.4% better than frequency). Importantly, the concreteness model predicts even better than the combined model that integrates all three predictors, suggesting that it is a dominant factor in determining source of semantic change across languages. Apart from these observations, valence has the next highest accuracy, whereas frequency performs poorly having an accuracy slightly above chance.

The idea of recovering media bias by embedding methods

Adhering to the purpose, which is to examine the citation patterns of 13 countries individually, Figures 10a and 10b, respectively, display the distributions of citations accrued by the countries and summaries of the citations per country. 10a, particularly, the number at the top of each box is the value used to determine the distribution outliers. 10a so as to observe clearly the international differences in the citation distributions among the 13 target countries. 3 together, on the one hand, we can deduce that the remarkable growth rate (in terms of research productivity) for Indonesia, Iran, Malaysia, and Saudi Arabia can be ascribed largely to regional publications. On the other hand, the countries, such as China, Hong Kong, and Taiwan, that had been consistently leading in Asian ‘language and linguistics’ studies for the past two decades had been also actively publishing articles in international journals.

First, we wish to assess the extent to which it is possible to reconstruct meaning to unattested proto-language states by using a phylogenetic comparative model. Moreover, we aim to study the evolutionary dynamics of various meanings from the perspective of semantic relations between them. This would enable us to gauge whether some semantic relations occur more frequently, something that would add new knowledge to the topic of semantic directionality.

A systematic synthesis and analysis of English-language Shuōwén scholarship

Interestingly, this representation of the current situation comes from online news, which may report what is currently happening more than depicting future scenarios—which may directly impact consumers’ opinions and economic decisions. The current study aimed at understanding the mechanisms of semantic evolution, ChatGPT App using a quantitative approach. However, since there are relatively few computational studies on semantic evolution, we believe that our study can contribute to the field. In this section, we will analyze results and point out some strengths and weaknesses of the performed model, method, data, and result.

The electrodes highlighted in red correspond to the example electrodes in the other pictures including in the Supplemental Materials unless otherwise noted. Established the algorithm model, designed the experiments and wrote the initial manuscript. The efficacy comparison among Perplexity-AverKL, Perplexity and KL divergence while setting more topic quantity. Meanwhile, informed consent was obtained from all participants and they agreed to the publication of identifying information/images in an online open-access publication. After manual parameter tuning, the determined thresholds remain within a reasonable range, as observed by the ROC curves. Once each model made its prediction for a given tissue, the background white pixels were again removed from prediction by ignoring all pixels where all RGB values were greater than 200.

Skip logic defined on KoBoToolbox is translated to REDCap branching logic and validation rules. The web-based Admin System was developed in C#32 and JavaScript33 programming languages to efficiently manage the mandatory metadata through create-read-update-delete (CRUD) operations. KoBoToolbox is a free, open-source suite of tools for data collection and elemental analysis developed by the Harvard Humanitarian Initiative. It was initially built for use in challenging environments in developing countries9. KoBoToolbox is powered by the Enketo open-source project29 and offers online and offline functionality and is accessible from any modern browser thanks to HTML5 features. The software relies on the XLSForm standard, which simplifies the authoring of forms in spreadsheets in a human-readable format30.

The requirements analysis results will be beneficial for elevator conceptual design. Accordingly, future studies are suggested to highlight the denoted differences of significantly attracted instances and meaning patterns of NP de VP construction in modern Chinese. For instance, Barrot (2017) and Lei and Liao (2017) each examine ‘language and linguistics’ research at the Asian regional level. Lei and Liao (2017) analyzed research trends in four Chinse-speaking regions—China, Hong Kong, Macau, and Taiwan. Based on their sample of 1381 articles and book reviews published between 2003 and 2012 in these regions, the analyses centered on productivity, research impact, and the journals in which articles from these regions were published most often. In a similar vein, Barrot (2017) conducted a bibliometric analysis of ‘language and linguistics’ research published in Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Viet Nam.

Strong performance on benchmarks such as General Language Understanding Evaluation (GLUE) and its successor (SuperGLUE) have also been obtained5,6. We have looked at a handful of approaches for interactive investigation using state-of-the-art machine learning technology. I hope the methods discussed in this article and the embedding-explorer Python package will be useful for you in your future research/work. semantics analysis The solution to this issue is to use sentence transformers, deep neural language models that produce contextually sensitive representations of text. They have outperformed all other approaches for a few years now, and have become the industry standard for embedding text. Now training such a model takes a lot of data, that we do not have at hand, but luckily we can use a handful of good pretrained models.

ThoughtSource: A central hub for large language model reasoning data

In this sense, REDbox modules were developed by analyzing the primary needs reported by researchers and research teams with considerable experience in TB. The authors participated in several interactions with independent teams to build the framework based on the REDCap and KoBoToolbox tools, which were identified as valuable assets in scientific research. With that in mind, REDbox was developed to fill in the gaps left by these tools and allow researchers to work seamlessly with these platforms. It is a suitable methodology because it simultaneously assists practical problem-solving, expands scientific knowledge, and enhances the respective actors’ competencies21. Considering that the research has a practical component in addition to its theoretical development, action research appears to be a good approach.

In conclusion, well-established semantic markers of PD can be captured automatically in connected discourse. In particular, disruptions in the construal of action concepts seem useful to identify persons with PD and to detect patterns that differ between those with impaired and spared cognitive skills. Given its objectivity, low cost, and scalability, this approach can fruitfully complement mainstream approaches to characterizing, phenotyping, and diagnosing patients.

Training corpus

Apart from mean differences in semantic effects, which might be difficult to find due to individual differences, predictions can also be made about correlations between different effects. These are interesting because rather than try to correlate people’s behavior on independent external measures, predictions about individual differences based on different aspects of the data in the same experiment can be done. Thus, participants who show no effect would simply become part of the distribution rather than adding to noise as happens when comparing means.

Second, due to how the list was constructed, it provides good control of important psycholinguistic variables such as word frequency and concreteness that may have profound effects on human and LLM ratings. That is, because low-sensibility phrases are created by reversing the word order of high-sensibility phrases, many lexical confounds related to psycholinguistic properties between the low- and high-sensibility phrases are eliminated. One way we may achieve this is by training a word embedding model from scratch on this corpus instead of using one that has been pretrained for us. In this example I am going to use the 20Newsgroups dataset as the corpus, in which we will explore semantic relations.

Thus, by including 30,515 articles from 13 of the most prolific Asian countries (in terms of research output), this study seeks to provide a good understanding of how ‘language and linguistics’ research has been executed in Asia. The current study also expanded the analytical foci, compared to these other studies; the current study executed bibliometric analyses comprehensively from the perspectives of not only productivity and research impact, but also authorship and collaboration patterns and research topics. This research studies the impact of online news on social and economic consumer perceptions through semantic network analysis. 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. We use an innovative approach to analyze big textual data, combining methods and tools of text mining and social network analysis. Results show a strong predictive power for the judgments about the current households and national situation.

By constructing a “media-event” matrix and performing Truncated SVD, we can uncover the underlying topics driving the media coverage of specific events. Our hypothesis posits that media outlets mentioning certain events more frequently are more likely to exhibit a biased focus on the topics related to those events. Therefore, media outlets sharing similar topic tastes during event selection will be close to each other in the embedding space, which provides a good opportunity to shed light on the media’s selection bias.

These algorithms were introduced to address challenges in processing timeseries data, such as high dimensionality and the need for computationally efficient feature extraction. While these classifiers offer advantages, it’s essential to note that their performance may vary based on the specific characteristics of the dataset and the requirements of the classification task. Female journalists elsewhere have described the phenomenon of not being assigned to cover “hard news” topics such as foreign affairs, crime, and economics, and being assigned to cover topics such as health, children, and education28,29. In our control analyses on non-parental reform articles using three days of randomly selected articles, we consistently found that female journalists authored around 30% of the articles.

Even in the aforementioned three countries, other languages are widely used co-officially and regionally; for example, Hong Kong now uses Mandarin as a co-official language alongside English, and Cantonese is widely used as a regional language. The authors wish to thank Vincenzo D’Innella Capano, CEO of Telpress International B.V., and to Lamberto Celommi, for making the news data available. The computing resources and the related technical support used for this work were provided by CRESCO/ENEAGRID High Performance Computing infrastructure and its staff. CRESCO/ENEAGRID High Performance Computing infrastructure is funded by ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programs. We then calculated the SBS indicators to measure the keyword’s importance and applied Granger causality methods to predict the consumer confidence indicators.

The time course of the microstates contains important information about the underlying neural generator (Khanna et al., 2015). In SCZ patients, the stability, tendency, intensity, or coordination of neural components are altered. Further, this paper analyzes the sensitivity and specificity of the method proposed herein when the EEG fragment length is 20 s. Sensitivity is calculated by comparing the number of persons correctly identified as having a condition in a test population with the true number of individuals who have the condition in the same test population. Specificity is calculated by comparing the number of individuals correctly identified as not having a condition in a test population with the true number of individuals who do not have the condition in the same population. The sensitivity of the model was calculated to be 97.1% and the specificity was 97.3%.

Let’s say that we have a a corpus of comments from a forum and we want to explore what kinds of associative relations can be found in them. Embeddings are numerical representations of textual data, and have become the canonical approach for semantic querying of text. In this article we will explore some of the ways in which we can explore textual data with the use of embeddings. During the tests, researchers monitored the dogs’ brain activity through non-invasive electroencephalography, or EEG. The traces revealed different patterns of activity when the objects matched or clashed with the words their owner said.

Finally, the validation phase was performed through a field test covering user training and satisfaction analysis. For a concise overview of the connectivity pattern (for visualization purposes), we also plotted the results from Fig. 5, outgoing information ChatGPT flow for each region is represented as an average of all information coming from that region, which can also be seen in Fig. Likewise, sent information is an average per region of information coming into a region, which can be gathered from the rows of Fig.

Differently, package qgraph and the function flow were employed to present the direct or indirect relation between SFM or POM and factors of social support, and self-acceptance. Large language models (LLMs) have shown remarkable abilities recently, including passing advanced professional exams and demanding benchmark tests. This performance has led many to suggest that they are close to achieving humanlike or “true” understanding of language, and even artificial general intelligence (AGI). Here, we provide a new open-source benchmark, the Two Word Test (TWT), that can assess semantic abilities of LLMs using two-word phrases in a task that can be performed relatively easily by humans without advanced training.