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

semantics analysis

The last component of the pipeline is a motor controller, which converts the speed and gait commands into motor torques. Similar to previous work, we use separate control strategies for swing and stance legs. By separating the task of skill learning and motor control, the skill policy only needs to output the desired speed, and does not need to learn low-level locomotion controls, which greatly simplifies the learning process. The sum of cosine similarity of tokens scores a tweet based upon a summation of the tweet’s component token vectors. However, the scalar value calculated using mean cosine similarity could disproportionately favor shorter tweets, as each token would contribute a greater proportion of the score.

semantics analysis

The Meaning in life questionnaire (MLQ) is a 10-item questionnaire, which was developed to measure participants’ level of meaning in life (Steger et al., 2006) and translated into Chinese by Chen et al. (2015). The MLQ concludes two subscales, namely the presence of meaning (POM) and search for meaning (SFM) and each subscale consists of five items. All items in the MLQ are rated on a 7-point Likert scale where 1 indicates “absolutely untrue” and 7 indicates “absolutely true.” Each subscale ranges from 5 to 35, with a higher score indicating ChatGPT App a higher level of POM or SFM. The MLQ was proved to have good reliability and validity among Chinese college students (Yang et al., 2023). The MLQ shows a good internal consistency reliability, with Cronbach’s α score for the whole scale is 0.868, for the POM subscale, 0.851, and for the SFM subscale, 0.889. Meaning in life, characterized as the concerns with the core significance and purpose of the personal existence of an individual, contains two factors, the presence of meaning and the search for meaning (Steger et al., 2006).

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It is important that the analysis functionality of this system be efficient at a level of computational infrastructure investment attainable in situations where funds and capability are limited on short notice9. Again, while corpora of millions or billions of lines of text are necessary to train more universal text recognition machine learning models, their efficiency can often be measured in hours or days10. “Analysing lexical semantic change with contextualised word representations,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Examples of semantic change (source meaning → target meaning) with directions congruent and incongruent with the prediction made under each variable. Customers benefit from such a support system as they receive timely and accurate responses on the issues raised by them. Moreover, the system can prioritize or flag urgent requests and route them to the respective customer service teams for immediate action with semantic analysis.

A bigger distance between a verb and its root hypernym indicates a deeper semantic depth and a higher level of explicitness. The WordNet module in the Natural Language Toolkit (NLTK) includes some measures previously developed to quantify the semantic distance between two words. Some of them are computed over semantic networks while others are combined with the notion of Information Content (IC) from information theory. Therefore, the current study chose Wu-Palmer Similarity and Lin Similarity as the measures employed in the analysis to include both types of measures.

Using murine models of pancreatic cancer progression and pancreatitis, we are working to develop and validate deep learning approaches that enable the rapid, reliable, and automated quantification of disease progression over large tissue areas, solely based on H&E staining. Murine models of pancreatic cancer were chosen as they have proven useful for mechanistic investigations of pancreatic cancer progression, modeling well the human disease both genetically and phenotypically, particularly during the evolution of pre-cancerous lesions8,9. The murine models have produced an explosion of studies including pre-clinical drug tests and evaluation of additional genetic perturbations that expose tumor-suppressing and tumor-promoting disease modifiers10,11,12. Synchronic linguistic studies aim to describe a language as it exists at a given time; diachronic studies trace a language’s historical development.

As such, their success in modeling neural activity would provide a counter-example to the claims in this paper. Yet, because of their complexity, it is virtually impossible to determine the precise role played by such abstractions in the computation of word probabilities, and for this reason we leave these models aside. The semantic role labelling tools used for Chinese and English texts are respectively, Language Technology Platform (N-LTP) (Che et al., 2021) and AllenNLP (Gardner et al., 2018). N-LTP is an open-source neural language technology platform developed by the Research Center for Social Computing and Information Retrieval at Harbin Institute of Technology, Harbin, China.

  • 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.
  • Theoretically, there should be 30 categories of shifts among different processes, as listed in Table 4, but many types were not present in this study.
  • By contrast, areas which were largely recipients of information were the left anterior temporal lobe and right middle temporal gyrus.
  • To our best knowledge, the present research is the first research to explore the symptom-level relations between self-acceptance, social support, and meaning in life, providing fresh insights into understanding the complex associations between the aforementioned variables.
  • An ontology comprises several properties, each describing a specific piece of data in the domain being represented18.

Our work provides a tool that is immediately applicable to the improvement and acceleration of pancreatic disease studies in animal cohorts, and provides workflows for similar tool development in other disease models. Moreover, the ease of use and availability allows for this tool to be a common thread for comparing different studies performed throughout the world. With the rise of historical linguistics in the 19th century, linguistics became a science. In the late 19th and early 20th centuries Ferdinand de Saussure established the structuralist school of linguistics (see structuralism), which analyzed actual speech to learn about the underlying structure of language. In the 1950s Noam Chomsky challenged the structuralist program, arguing that linguistics should study native speakers’ unconscious knowledge of their language (competence), not the language they actually produce (performance). His general approach, known as transformational generative grammar, was extensively revised in subsequent decades as the extended standard theory, the principles-and-parameters (government-binding) approach, and the minimalist program.

However, similar to many psycholinguistic studies, ratings were gathered in a relatively homogenous population (undergraduate students). Interesting directions for future research could include novel two-word phrases, metaphorical semantics, understanding combinations of more abstract concepts (e.g., love, freedom, etc.), or how ratings gathered from more diverse human populations may relate to LLM performance. Based on the above results, it can be concluded that CT do show several distinctions from semantics analysis both ES and CO at the syntactic-semantic level, which can be evidenced by the significant differences in syntactic-semantic features. These distinctions partially support the hypotheses of “the third language” and some translation universals. Table 4 shows that CT exhibit average Wu-Palmer Similarity and Lin Similarity values notably similar to those of CO, which is logically consistent as both text types operate within the same language system, inherently sharing linguistic characteristics.

Theory, model, data, method, and analysis

In this study, we are mainly interested in rates of change, which we equal to loss rates, i.e., the probability to lose a meaning after having the meaning P(0|1). The information about keeping meaning is coded by the attested semantic relation (0) as well as in the probability P(0|0). Etymology is a tricky and complex issue, and the results of our reconstruction will not solve this problem—the probability results will not replace or even improve the reconstructions by the comparative method.

Unfortunately to everyone, gaining meaningful insight to written natural language is not a trivial task by any measures. Close reading is of course an option, but you would ideally prefer to look at textual data through a more macro-analytical/quantitative lens as well. Not to mention that in the age of big data close reading is rarely a feasible option. We performed statistical analysis on the surprisal values calculated using the N-gram, Lexical, POS, and Syntactic surprisal of the 5 classes of stimuli (Strong_PRED-NP, Strong_PRED-VP, Weak_PRED-NP, UNPRED-NP, UNPRED-VP) relative to the first and the second word of the HPs. This analysis aimed at identifying the statistical language model that best differentiated between various linguistic stimuli in the same way as a human listener would do (e.g., distinguish Strong_PRED-NP and Strong_PRED-VP but not UNPRED-NP and UNPRED-VP). For semantic adjuncts, the results show that the p-values of the comparison between the ANPS of adverbials (ADV) and manners (MNR) are smaller than 0.05.

semantics analysis

The scientific community widely uses the REDCap system to collect and manage research data, allowing researchers to conduct their studies independently. However, the software may present some usability issues during data collection, such as a polluted graphical interface, gradual performance degradation, and the lack of offline operation without depending on a mobile application. For the validation phase, the modules developed by the authors were used in five cross-institutional TB research projects in Brazil (see Table 3). Also, it is demonstrated how semantics can promote the reusability and interoperability of research data. The solution has research teams, research centers, and study participants as stakeholders.

While the political parties presented different arguments in their discussion of the parental leave reform, we noted only a small effect of newspaper orientation, relative to journalist gender, in our semantic analysis. The newspapers in our analysis report their general political orientation but cannot be considered as perfect mirrors of the stances and views of political parties and actors, as party websites and professional social media accounts might be. An analysis of the semantics of communication directly from political parties, rather than an indirect newspaper categorisation as we have done here, might result in a stronger ChatGPT political orientation effect. Finally, both sets of political arguments on parental leave reform have emotive dimensions, which may account for the lack of sentiment difference between the left and right-oriented newspapers. The current study explored the relationship between self-acceptance, social support, and meaning in life using symptom network analysis with college students as subjects. The analysis found that “SIA” (Self-acceptance) was the key bridge symptom in the symptom network of self-acceptance and social support and it can be an important targeted symptom when improving both social support and self-acceptance.

They showed a strong effect of priming that appeared early, but there were no interactions with consistency. These results thus show no evidence of early interactions between word type and semantics. There was also a weak early consistency effect which, as far as we are aware, has not been reported.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Targeting their needs and identified challenges (see Tables 1, 5), REDbox delivers useful functionalities for the collection and management of research data and promotes the availability and reusability of research data. Therefore, the need for an accommodating option to conduct research and promote data sharing in TB services led to the conceptualization of the open-source solution proposed in this work. There was none found in the literature, and after rounds of discussions with researchers, developing a module-based and customized software to overcome existing technological barriers in TB services was defined as the main challenge to overcome.

In relation to previous studies

Beforehand, the current study found that Scopus provided author-defined keywords for only 27,214 of the 30,515 articles. For the remaining 3301, for which the keywords were unavailable in Scopus, the keywords were extracted using KeyBERT (Giarelis et al., 2021). KeyBERT is a keyphrase extraction method that relies on a deep learning-based BERT (Bidirectional Encoder Representations from Transformers) algorithm (Devlin et al., 2019), which is widely used in document summarization. Because KeyBERT is based on BERT’s pre-trained model, it is both efficient and can create N-gram keywords (Arhab et al., 2022; Khan et al., 2022), like the author-defined keywords of the target articles.

ChatGPT Prompts for Text Analysis – Practical Ecommerce

ChatGPT Prompts for Text Analysis.

Posted: Sun, 28 May 2023 07:00:00 GMT [source]

To train the logistic regression model combining all the three predictors, we reversed each of the semantic shifts with 50% probability, and trained the models to classify whether a shift had been reversed or not. We performed a 5-fold cross-validation and averaged the accuracy to measure model performance. Under this scheme, we select alternative targets from the test set that are as close as possible in similarity to the source as the actual target. In other words, we control for semantic similarity in the pool of candidate targets. For example, consider the source “Jupiter” and the target “Thursday,” which have some distance in semantic space. We would select candidate targets such as “latitude,” “molar tooth,” “place,” and “blood,” which are all the same distance from “Jupiter” in semantic space as “Thursday,” respectively.

We found that Dot Product with a word window size of 8 resulted in the maximum AU_ROC. We saw that the appropriate minimum word frequency varied depending on the scalar comparison formula. The optimum value for minimum word frequency for Dot Product was found to be 3 whereas the optimal value for all other formulas was 8. This indicates that the performance of the model is tied to the scalar comparison used and its optimal setting. The default setting of 100 dimensions proved to be adequate for the hidden layer dimensionality setting. Negative sampling showed substantial improvements across all scalar comparison formulas between 0 to 1 indicating a minimal number of negative context words in the training has an overall positive effect on the accuracy of the neural network.

The contributions of this work include methods to determine comparative relatedness between a single word and a micro-blog post (e.g. Twitter, etc.) within a temporal context, i.e. can the meaning of a tweet be derived when the time of composition can affect the interpretation of what is said. The first is predicated upon the premise that large comprehensive corpora must rely upon a probabilistic determination of meaning for homonyms. That is to say, absent extensive context, a word with two disparate meanings may be interpreted incorrectly if one meaning occurs more frequently within a corpus than the other. Secondly, as word embeddings rely upon a vector to describe meaning, this paper attempts to determine the best linear operations for comparisons of a single word embedding to multiple word n-grams within the same vector space. In summary, we have offered an automated approach and a comprehensive evaluation of multifaceted regularity in semantic change across languages. We believe that our study paves the way for diversifying the scientific inquiry into semantic change beyond individual or a restricted set of languages.

Correlations between the size of early priming effects between the two prime groups with inconsistent words were also very weak, suggesting early semantic effects with inconsistent words were not predictable by individual differences. Alternatively, there was a moderate strength correlation between the size of the priming effect with consistent and inconsistent words in the related/unrelated prime group on the N400. This offers a possible locus of individual differences in semantic processing that has not been previously reported. It can be used to both mark the localization of tissue features and quantitatively to measure the extent of disease based on multiple histologic features (Supplemental Fig. 2). Such rapid and unbiased quantification of disease states in animal models is critical to enabling efficient and accurate disease assessments among large study cohorts, as well as provide a common method to compare finding across different studies. The ability of this tool to accurately predict histologic features among 25 unseen pancreatic pre-cancer samples from multiple time points and 9 unseen samples comprising other disease states demonstrates the robustness of the models when analyzing new datasets.

These results have important implications for the accuracy and reliability of microstate analysis. In this section, microstate feature extraction will be performed for these two microstate sequences, and these features will be used for the identification of SCZ in the subsequent sections. A total of 39-dimensional microstate features were extracted as inputs to the classifier in this paper, including 16-dimensional quality features, 21-dimensional semantic features, and 12-dimensional traditional temporal features. (3) After calculation, the healthy group and the patient group, respectively, obtained corresponding microstate templates, each template containing four microstates. Assign the most relevant microstate labels based on the spatial correlation between the topology of the EEG at each time point and the four microstates.

Thus, the phrase cosine similarity is used as a real number representing how close two terms are within the context vector space. Two similar or related terms will have a cosine similarity as a real value close to one, where two lesser-related terms will have a lower cosine value, to a minimum at negative one. Envisioning each term within the context of a corpus as having a vector, and that vector’s spatial position related to the term’s context or meaning allows the relatedness of two vectors to be interpreted as inversely proportional to the degree of the internal angle formed by the two vectors. Backpropagation occurs via stochastic gradient descent, and the process begins again with the next word within the context window. Once all context terms are processed within the word window for the center word, the process begins again with the next center word and its context words. To begin this process, the vocabulary of the corpus is defined and its size determined.

Euclidean distance was calculated for each pair of words as a proxy for dissimilarity and later converted to similarity for ease of interpretation. Lighter colors reflect pairs that are closer in semantic space, darker colors reflect pairs that were further away in semantic space. Note that the vectors displayed in this figure only show 40 values; the true imputation process would include all 120 potential similarity values. While researchers have increasingly acknowledged the interdependence of episodic and semantic memory, there are relatively few studies of the testing effect that directly manipulate the semantic information within to-be-learned pairs of items28 or integrate its role into mechanistic accounts.

semantics analysis

In each counterbalanced group, one set of unrelated words and one set of nonwords was were used as primes, and a matched set of unrelated words and nonwords was used with the second set. A COS-7 cell expressing mEmerald-Sec61b was imaged over 43.5 s at 1.5 s per frame (Fig. 2a). A COS-7 cell expressing mEmerald-Sec61b was imaged over 43.5 s at 1.5 s per frame (Fig. 2d).

Total tissue (DAPI positive) region was also calculated by finding all pixels where RGB values were lower than 200. To combine all four tissue masks, normal acinar predictions override metaplasia and dysplasia predictions; metaplasia predictions override dysplasia predictions; normal acinar, metaplasia, and dysplasia predictions all override DAPI predictions. In his career, Khandelwal has gained extensive experience in leading and managing teams, architecting big data analytics solutions, and designing and developing business intelligence and data analytics solutions based on Adhoc Analytics using OLAP and data mining, Hadoop, and Spark. Realizing the promise of semantic layers, multiple BI tools and data discovery platforms implemented semantic layers within their products over the past decades and quickly became popular among business users. A semantic layer is a layer of abstraction that separates the physical view of data from the view seen by business users. By providing a more business-friendly representation of data, it acts as a bridge between the raw data and the business users.

One of our initial aims was to look for cultural explanations of change rates (2.1–2.2). It is tempting to think of cultural explanations when one realizes that, e.g., CATTLE and SMALL CATTLE have higher change rates than wild animals (PREDATORS and GAME ANIMALS), WEAPONS and TILLAGE higher change rates than METALS, and so forth. All these cultural factors are discussed in the original publication of the data (Carling, 2019) and it is also clear that cultural factors have an impact on borrowing, as is demonstrated in another study on our data (Carling et al., 2019a). For that purpose, we initiated a coding of all concepts meanings pertaining to cultural factors.

Transferring metafunctions between English and Chinese—A functional linguistic approach to translation studies (dissertation). Transitivity process theory and English-Chinese semantic functional equivalence translation. The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author/s. This example of exploring the field is part of Xi’s address at the headquarters of UNESCO in 2014. Here, the prose in ST was invoked to stress the idea that all civilizations and cultures together make the world diverse and beautiful and thus, all countries need to work together.

This example proves that the informational structures in the translated texts are significantly simplified by reducing the number of nested sub-structures in semantic roles. Table 3 indicates that significant differences between CT and ES can be observed in almost all the features of the semantic roles. For core arguments that are the main components constituting the semantic structure of a sentence, the differences in all the features add weight to the proposition that information structures of sentences in CT exhibit characteristics substantially different from those in ES for several reasons.

Nominalization is one of the ways to realize such a trend, changing the Chinese verbal groups that serve processes in clauses to English nominal groups that can form embedded clauses in one complicated sentence. Third, the high frequency of material and relational processes distributed in the ST can also be the reason for participant and circumstance nominalization in many cases occurring in these two types of clauses in the ST. There is a tendency toward a large proportion of the process compression resulting from the differences between Chinese and English. By contrast, English is a language of hypotaxis that values the sequence of different levels of ranks and does not prefer the use of too many verbs or verbal groups in one clause, leading to many Chinese processes being cut in English clauses. Further, when the rhetorical device of repetition is used in Chinese to stress the original meaning or to show the power of the language itself, processes are also inclined to be reduced or omitted, for that figure of speech is sometimes regarded as redundancy in English expressions. Some (e.g., Huang, 2002, 2003, 2004; Zhang, 2010) suggest that the formal equivalence of experiential meaning should be fulfilled to offer a better literary translation.

Compared to the original meanings extracted from dictionaries and fieldwork of the database, the data of the Mouton atlas contained a substantial standardization of dictionary meanings (Carling, 2019). However, this standardization was not sufficient for the current project, and therefore, a further standardization and simplification was necessary. The standardization process was done systematically by searching the meaning fields of the data. Redundant explanations were removed, very close synonyms were conflated, and references were standardized (e.g., “young animal”, “young of animal”, “young of the animal”). In the process, external databases such as CLICS were consulted, but the process was mainly done by internal leveling of redundancy.

semantics analysis

There are altogether 4 argument structures nested in the English sentence, with each semantic role in the structure highlighted and labelled. 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. This publication would not have been possible without the extraordinary work of ECFR’s Unlock team, particularly Pawel Zerka, who offered key analytical insights into the data and helped sharpen the authors’ arguments. Adam Harrison was a brilliant editor of various drafts and greatly improved the narrative flow of the text. Andreas Bock led strategic media outreach, Nastassia Zenovich visualized the data, Nele Anders and Julie Morgan managed advocacy, while Anand Sundar navigated successive drafts. The authors also thank Paul Hilder and his team at Datapraxis for collaborating on developing and analysing the European polling referred to in the report.

Second, this study utilized self-report questionnaires to measure self-acceptance, social support, and meaning in life, which is inevitably affected by the daily emotions of participants and the social desirability bias. To mitigate this limitation, future research can employ experiments or add objective indicators to explore the relations between social support, self-acceptance, and meaning in life. Third, age, cognitive styles, and personality would affect the meaning in life (Allan et al., 2015; Pang et al., 2019) and the associations between social support and self-acceptance.

The fact that the models generalize well, despite being trained with a relatively small dataset (Supplemental Fig. 4 and Table 3), illustrates the effectiveness of this workflow for tool development. Using this workflow (Supplemental Fig. 4) makes niche tool development plausible for small working groups that might have less access to the resources needed to produce large batches of annotated data. This pipeline is also faster, cheaper, and more generalizable than immunostaining, which can take days and be prone to investigator bias. This will allow working groups to digitally process many samples within hours instead of spending days immunostaining individual samples.

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