Detecting Parkinsons disease and its cognitive phenotypes via automated semantic analyses of action stories npj Parkinson’s Disease

The most well-known of these is the N40021, which occurs around 400 ms. However, a number of studies have suggested that various types of lexical or semantic access occur much earlier, typically in less than 250 ms and often even earlier22,23,24,25,26,27,28,29,30. Another component that is less commonly examined in reading studies that is not necessarily related to semantics directly but may offer evidence that early semantic processing could occur is the P2. She found that when her participants showed a behavioral effect of spelling–sound inconsistency when reading low frequency words, they also showed a similar effect on the P2 that occurred 168 ms after word onset.

Carpenter33 proposed that retrieving information from memory necessitates elaborative processes that induce spreading activation to semantically related information34,35, which can provide additional retrieval cues31. Consistent with this framework, one recent study showed that when to-be-learned images do not contain meaningful semantic information, there is no benefit for retrieval practice compared to restudying the images36. A separate account suggests that testing supports memory by facilitating semanticisation37 (i.e. a shift towards more generic semantic representations as opposed to detail-rich episodic representations) and relational processing, which promotes attention to semantic information38. In the end, despite the advantages of our framework, there are still some shortcomings that need improvement. First, while the media embeddings generated based on matrix decomposition have successfully captured media bias in the event selection process, interpreting these continuous numerical vectors directly can be challenging.

Here is a screenshot of me looking at what networks arise around the words “jesus”, “science” and “religion”. You might be familiar with Word2Vec, which is the method that popularized the use of static word ChatGPT embeddings in research and practice. What if, instead of wanting to transfer general knowledge into a specific model, we just want to get a mapping of the semantically specific aspects of a smaller corpus?

Where people around the world find meaning in life

You can foun additiona information about ai customer service and artificial intelligence and NLP. Meaning in life is an important yet declined mental resource for the well-being and comprehensive development of college students (Huang et al., 2022; Olstad et al., 2023). Self-acceptance and social support may serve as two possible sources from which college students may attain more meaning in life (King and Hicks, 2021). To prompt the exhibition of meaning in life among college students, the relation between meaning in life, self-acceptance, and social support needs to be clarified. Finally, the Graves et al.21 dataset represents one of the largest sets of combinatorial semantic phrases that have human ratings, and the nature of its construction means that low- and high-sensibility phrases are closely matched to each other in important psycholinguistic variables.

• While this paper agrees with the assessments of this work, it seeks to expand upon their research and provide a possible method for parsing social media information in a rapidly changing context.
• Advances in deep learning technologies are creating opportunities for the rapid and objective assessment of both normal tissue and pathologic processes in biologic specimens.
• For example, study43 showed that different frequency bands are involved in the bottom up and top down processing of natural reading states.
• In addition, China, Hong Kong, and Israel were the most active in publishing articles in international journals, whereas Indonesia, Iran, and Malaysia concentrated on publishing articles in regional journals.

Compared with the previous EMD decomposition (Siuly et al., 2020), signal energy or frequency analysis methods (Devia et al., 2019; Akbari et al., 2021), the micro-state method adopted in this paper is significantly improved. Compared with the existing microstate analysis techniques (Baradits et al., 2020; Kim et al., 2021), the sensitivity of the microstate sequence to the template is used to greatly enhance the recognition of SCZ. The experimental results fully demonstrate the effectiveness of the proposed indicators, which provides an effective basis for the clinical diagnosis of schizophrenia and the realization of intelligent diagnosis.

Investigating Corpus-Level Semantic Structure with Document Embeddings

The first dataset is the GDELT Mention Table, a product of the Google Jigsaw-backed GDELT projectFootnote 5. This project aims to monitor news reports from all over the world, including print, broadcast, and online sources, in over 100 languages. Each time an event is mentioned in a news report, a new row is added to the Mention Table (See Supplementary Information Tab.S1 for details). Given that different media outlets may report on the same event at varying times, the same event can appear in multiple rows of the table.

This section describes how we conducted our research, including data collection, ontology development, dataset generation, and analysis methods. Figure 1 represents the structure of the study for the data acquisition, processing, synthetic data generation and comparative analysis. This study used the Standards for Reporting Implementation (StaRI) checklist (see Supplementary Material-1). Data collection has been carried out in accordance with relevant guidelines and regulations in the “Ethics approval and consent to participate” section under Declarations. According to the scientific database searches, many articles reported their experiments on activity datasets collected by different wearable activity devices; however, most of the datasets are private; therefore, results are difficult to replicate or extend.

Topic modelling was used to examine differences in language patterns between the articles published in left and right-oriented newspapers, and written by male and female journalists, using Latent Dirichlet Allocation (LDA) modelling. LDA is a generative statistical model, whereby a document is considered as a distribution over topics, while a topic is a distribution over words25. To limit our analysis to meaningful differences in language use, we lemmatized all tokens and removed stop words26.

Since the context of abstract words is, unlike that of concrete words, mainly tied to an individual’s experience, their combinations of the underlying neural systems are weighted differently than those of concrete words. Along these lines, several studies have found evidence that seemingly contradicts the pattern predicted by the dual coding theory. Some indicate that greater activation can be observed in the left temporal areas, such as the left basal temporal cortex, for concrete and stronger activations in the right temporal areas for abstract words17,18. Others have found greater activity for abstract words in the right hemisphere7 (for a comprehensive review see19,20). Currently, these studies do not offer a converging answer as to which neural patterns underly word-processing, which is why existing data can be interpreted in the context of either the dual coding or context availability theory7. These inconsistencies can be attributed to many factors, including the variation in task or stimulus material17 and the limited temporal resolution of tools such as fMRI and PET which fall short in capturing the temporal development during word comprehension.

TDWI Training & Research Business Intelligence, Analytics, Big Data, Data Warehousing

The two dimensions of language models (linear vs. hierarchical structure and word vs. category prediction). A choice in each dimension yields a distinct model of language, from which we can extract probability values. In this example, the contextual need for de-nominalization is overshadowed by the “connectivity effect”, causing the translation to retain the nominalization and the predicate “is” from the source text. This leads to an idiosyncratic information structure in the target language and hence, the deviation between the translated and target languages. In our further exploration of specific semantic roles, results of the Mann-Whitney U tests in Table 6 show that there exist significant differences in most features across various semantic roles, suggesting that CT are quite distinct from CO in syntactic-semantic strictures. Table 2 shows that the average number of semantic roles per sentence (ANPS) of CT is approximately the same as that of ES.

The model has the problem that it cannot distinguish precisely which meaning diachronically changed into which meaning inside the etymological tree. Therefore, the model estimates semantics analysis the change type at branches between nodes based on the meanings of the attested languages. The probability is calculated individually for each lexeme and each meaning in the data.

Translation universal hypothesis

Now you can picture taking the first vertical slice from U, weighting (multiplying) all its values by the first singular value and then, by doing an outer product with the first horizontal slice of ?-transpose, creating a new matrix with the dimensions of those slices. Or, if we don’t do the full sum but only complete it partially, we get the truncated version. The matrices ChatGPT App ?? are said to be separable because they can be decomposed into the outer product of two vectors, weighted by the singular value ?i. Calculating the outer product of two vectors with shapes (m,) and (n,) would give us a matrix with a shape (m,n). In other words, every possible product of any two numbers in the two vectors is computed and placed in the new matrix.

The outcome of our study and the results will be further described below under Section 3. We have selected the data partly because of its status (presence of etymology and polysemy), partly because of its cross-linguistic nature (involving several families). Due to its status of being restricted to one continent (Eurasia), we do not aim to establish any universal tendencies by our results. Nevertheless, we believe that the data is rich enough to observe general tendencies in lexical semantic evolution.

The flipside of this may be that they are not yet ready to consider major compromises to bring the war to a conclusion. When asked to choose between obtaining NATO membership and relinquishing occupied territory, seven in ten Ukrainians say they would not support an offer to enter NATO tomorrow in exchange for agreeing to give up on occupied territory. It is true that President Volodymyr Zelensky’s image has been tarnished by the burdens of office. The poll shows that only 34 per cent of Ukrainians currently say they trust him “a great deal”. But a further 31 per cent trust him “quite a lot” – meaning that, by two to one, those who are keeping the faith with their leader outnumber those who are not.

These existing literatures together suggested a higher rate of journal and article coverage in Scopus than in WoS. In the Social Sciences and the Humanities, especially, where ‘language and linguistics’ are strongly related, Scopus covered almost twice the number of journals than WoS. For articles published by Asian countries, Scopus has higher coverage as well (Martín-Martín et al., 2018; Mongeon and Paul-Hus, 2016).

Researchers have found that, compared to the control group, SCZ patients exhibit continuous increases in the time coverage and occurrence rate of Microstate C, while the time coverage and average duration of Microstate D significantly decrease (da Cruz et al., 2020). Leveraging changes in microstate parameters in SCZ patients, scholars have utilized microstates as crucial neural imaging biomarker in the automated identification of schizophrenia (Baradits et al., 2020; Luo et al., 2020; Wang et al., 2021). For instance, Baradits et al. (2020) used four microstate time parameters as features for SCZ classification and achieved 82.7% recognition accuracy. Kim et al. extracted 19 microstate features and 31 traditional EEG features from the resting-state EEG of SCZ patients, combining them with machine learning for identification. The results demonstrated that microstate features (76.62%) outperformed traditional EEG features (68.89%) in SCZ identification, displaying better classification performance. This suggests the potential of microstates as valuable neural imaging biomarkers for brain disorders, enabling a more effective representation of patients’ abnormal states (Kim et al., 2021).

Relationship extraction is a procedure used to determine the semantic relationship between words in a text. In semantic analysis, relationships include various entities, such as an individual’s name, place, company, designation, etc. Moreover, semantic categories such as, ‘is the chairman of,’ ‘main branch located a’’, ‘stays at,’ and others connect the above entities. No feedback was provided, as feedback can provide an additional restudying opportunity that can enhance final test performance for tested items68 and inflate testing effects28.

With events occurring in varying locations, each with their own regional parlance, metalinguistics, and iconography, while addressing the meaning(s) of text changing relative to the circumstances at hand, a dynamic interpretation of linguistics is necessary. Our computational approach also differs substantially from a large body of work in natural language processing that uses historical corpora as a primary medium for investigating semantic change. It is an open issue how the regular patterns identified here with discrete word senses may be leveraged to develop novel algorithms for the automated inference and generation of semantic change in naturalistic settings. For our purposes, we filtered the data to focus on unidirectional shifts with the same language and word for both the source and target senses, and we focused on the shifts of the types synchronic polysemy and semantic evolution. These types include cases in which there are no morphological processes involved (contra derivation) and the processes happen within the same language (contra borrowing and cognate).

(B) The expected value of sentiment in news articles related to the parental leave reform compared to the “General News” control article set. While paid leave for fathers after the birth of a child has become increasingly available, mothers still take most of the parental leave. A recent European Union (EU) reform addresses the unequal sharing of leave between parents via earmarking of paid, non-shareable leave to each parent. Given that the reform’s success will depend on uptake by families, we analysed Danish national media coverage to understand how journalists were writing about the reform. We assessed the sentiment and semantics of leave reform coverage compared to general news from the same period, also considering the inferred journalist gender and newspaper political orientation. Parental leave reform articles were slightly more emotional than general news, independent of who authored the article, or the newspaper where it was published.

For example, we can analyze the time-changing similarities between media outlets from different countries, as shown in Fig. In contrast, our method, based on word embeddings (Le and Mikolov, 2014; Mikolov et al. 2013), directly models the semantic associations between all words and entities in the corpus with a neural network, offering advantages in capturing both semantic meaning and holistic nature. Specially, we not only utilize word embedding techniques but also integrate them with appropriate psychological/sociological theories, such as the Semantic Differential theory and the Cognitive Miser theory. In addition, the method we propose is a generalizable framework for studying media bias using embedding techniques. While this study has focused on validating its effectiveness with specific types of media bias, it can actually be applied to a broader range of media bias research.