A semantic analysis-driven customer requirements mining method for product conceptual design Scientific Reports

The Predictions can also distinguish histologic features differences that the manual annotations combined. As observed in Table 2, the models implementing Reinhard normalization achieved better scores on average, relative to Vahadane and Macenko. Furthermore, the models achieved the best scores when the normalization process was applied on intermediate crops rather than across the whole image. This is because staining can be uneven within a single section, and normalizing crops helps to overcome these differences in intensity; whereas, normalizing across a whole section only helps overcome differences between images. The challenge of objective quantification of tissue changes among animal cohorts is significant.

Moreover, analyzing customer reviews, feedback, or satisfaction surveys helps understand the overall customer experience by factoring in language tone, emotions, and even sentiments. The SWAT was performed at the beginning of Day 1, prior to learning word pairs, and again at the end of Day 2, after the final test. On each trial, participants received 60 words in a “word bank” on the left side of the screen. Participants clicked on a word to bring it over to a main arrangement area (“the canvas”) and then dragged each word to the location of their choosing. Participants were instructed to take as long as they needed to arrange the words such that more similar words were closer together and more dissimilar words were further apart.

Testing theory of mind in large language models and humans

In summary, the findings presented in Table 2 indicate that 27% of the selected keywords have a Granger-causal relationship with the aggregate Climate. This percentage is consistent with the results obtained when evaluating Granger causality for the Current dimension of the survey. These results suggest that a significant portion of the selected keywords can be used to predict changes in the Climate dimension, providing valuable insights for future research and decision-making. Our tests indicate that a higher number of keywords could impact how consumers perceive the Future situation. However, the most significant impact appears to be on the personal climate, as evidenced by 61% of significant Granger causality tests. To measure whether the SBS indicators offered relevant information to anticipate our economic variables, we performed Granger Causality tests.

With the development of deep learning and high-performance GPU, plentiful neural network models with more layers and more parameters are proposed. However, it is difficult to achieve satisfying result without a large number semantics analysis of data for model training. Namely, the neural network structure parameters are trained in advance through a large amount of data, and then the trained neural network is fine-tuned under the current specific task.

REDbox combines elements of the REDCap and KoBoToolbox electronic data capture systems and semantics to deliver new valuable tools that meet the needs of tuberculosis researchers in Brazil. The framework was implemented in five cross-institutional, nationwide projects to evaluate the users’ perceptions of the system’s usefulness and the information and user experience. Seventeen responses (representing 40% of active users) to an anonymous survey distributed to active users indicated that REDbox was perceived to be helpful for the particular audience of researchers and health professionals. The relevance of this article lies in the innovative approach to supporting tuberculosis research by combining existing technologies and tailoring supporting features. This occurred despite the design being potentially able to produce significant results even when differences between means are not found. The Bayes Factor results also provided anecdotal evidence for the null effect in all regions examined.

Learning to Walk in the Wild from Terrain Semantics

This would be a relevant effort, since standard screening instruments, such as the Mini-Mental State Examination, are bound to ceiling effects and often fail to capture cognitive dysfunction in PD26. Given that dementia symptoms may be unnoticed in over half of PD patients27, our semantic framework may be combined with other approaches, such as motor speech assessments28, ChatGPT to establish phenotypic distinctions within the overall patient population. Action-concept outcomes are useful targets to identify Parkinson’s disease (PD) patients and differentiate between those with and without mild cognitive impairment (PD-MCI, PD-nMCI). Yet, most approaches employ burdensome examiner-dependent tasks, limiting their utility.

Volume two (Xi, 2017a) compiled Xi’s critical speeches and writings from August 2014 to September 2017, and Volume three (Xi, 2020a) from October 2017 to January 2020. You can foun additiona information about ai customer service and artificial intelligence and NLP. All the works were translated into English (Xi, 2014b, 2017b, 2020b) and released globally. All data analyzed in this study are cited in this article and available in the public domain.

However, it has not been comprehensively investigated whether there is shared regularity in source-target mapping of diachronic semantic change across languages. It is also an open question whether and how new mappings between a source meaning and a target meaning can be automatically inferred in semantic change. Characterizing these fine-grained, regular meaning mappings in semantic change can help inform the generative processes that give rise to novel instances of semantic change. To further probe the effects of learning on semantic representations and representational change, we performed a series of LMMs, using the lmer function from the lmerTest R package76 to estimate fixed and random effects. For each model, we included predictors of relatedness (related vs unrelated), learning condition (tested vs restudied), and recall success at Day 2 (recalled vs forgotten).

Moreover, the PDC is computed in the frequency domain enabling us to find distinct patterns of connectivity in different frequency bands. Furthermore, we adopted an extended version of PDC that is time-varying and multi-trial, both characteristics suited for this study. The former because in cognitive processes like word comprehension, functional brain networks change on a sub-second temporal scale56. In order to capture these transient alterations in connection strength, time-varying versions of the PDC algorithm have been developed57,58. The latter because our algorithm is trained on multiple trials (i.e. multi-trial approach).

We deviate from this approach by instead using LDA to estimate the most salient partition of the data (i.e., words in newspaper articles) into two topics. Notably, our model does not include any information regarding the gender of the author or the political orientation of the newspaper. The model estimated the probability of belonging in topic one and topic two for each word and each article (Topic 1, Topic 2). We then pooled the distribution of mean estimates for the probability of each article as belonging to Topic 1 or Topic 2 based on the newspapers’ political orientation and journalist gender (i.e., four groups). If there are meaningful differences between the mean estimates of the different groups, this would indicate semantic differences in how they report on the parental leave reform.

Indeed, while PD patients are consistently affected in this category1, they evince no major alterations in more general semantic measures, including processing of abstract12 and social concepts9, semantic granularity29, and ongoing semantic variability29, among others. Note, also, that the P-RSF metric allows identifying specific semantic memory domains that are compromised and spared, favoring interpretability. Taken together, these observations attest to the distinct usefulness of our methodological framework. Also, the P-RSF metric systematically outperformed classifiers based on GloVe embeddings. First, this was the case when such embeddings were used to calculate distance between verbs in the retellings and in the original texts.

• We repeated the bootstrap analysis for 15, 25, …, to 95% of all available trials to estimate the variability.
• The second one delivers a user-friendly interface and natively works offline through a mobile browser but has limited features for data management.
• 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.
• This formula was selected to leverage the efficiency of optimized pre-generated code over other possible functions.

To this end, we used Global Vectors for Word Representation (GloVe), a method that captures linear substructures of a text’s word vector space based on summated statistics of the co-occurrence between any two words in a corpus58. The same part-of-speech-tagger used in our main analyses was employed to find all verbs in each preprocessed retelling. Then, the numerical representation of all verbs in each retelling was obtained using a previously reported GloVe model, pre-trained with the Wikipedia 2018 Corpus, which contains ≈709 million Spanish words59. We computed the cosine distance between each verb in the retelling and the verbs in the original story (i.e., the same verbs used in our main analyses).

In Analysis 1, cells outlined in yellow highlight the pairwise distance of the cue word GENDER to its target FEMALE, and we compare how this distance changes across learning. In Analysis 2, we examine the change in pairwise distance across learning between cue words (e.g., GENDER) and semantically related non-target words (lures; green outlines). We define the representation of an individual word as its row vector from the RSM (i.e. by its pairwise relationships to all other words in our set). In Analysis 3, we test how the representation of each word changes across learning by taking the Pearson correlation of the row vectors from the pre- and post-learning RSMs.

REDCap offers the possibility to include annotations for each field, which will not be displayed on the form or survey but will be available to the designer and in data exports to help understand the data27. This annotation can be a property of an ontology or an HXL hashtag, depending on the user’s preference. Some obstacles make it difficult to make data available, such as using non-standardized vocabularies/terminologies, using legacy systems, and the enormous bureaucracy involved in accessing health data. Although complex, sharing health data can enhance research activities and increase a health service’s clinical and operational effectiveness24. Data sharing requires functional and semantic interoperability capabilities to properly communicate and understand the data25,26.

Patients were diagnosed based on United Kingdom PD Society Brain Bank criteria37, with motor assessments via the UPDRS-III38 and the Hoehn & Yahr scale39, and executive function testing through the INECO Frontal Screening (IFS) battery40. No patient had primary language deficits, signs of Parkinson-plus, deep brain stimulation antecedents, or concomitant neurological, psychiatric, or addiction disorders. Results from the Barthel Index41 and the Lawton & Brody Index42 indicated that all patients were functionally independent.

Copula allows us to decompose a joint probability distribution into marginal values of uncorrelated variables and functions that “couple” these marginal values together. The high-level steps for synthetic data generation with GC method have been detailed in Textbox 3. We used python SDV package18 and “GaussianMultivariate” method to generate synthetic data with GC. Currently, research on microstates has predominantly concentrated on the neural representations of individual microstates, with limited attention dedicated to microstate sequences.

This value was chosen as a lower bound on vector representation of similarity, as included values would be closer to coincident than orthogonal. The nodes are subjected to a gravity algorithm to encourage similar terms to cluster, and dissimilar terms to repel each other. The edges in this graph represent the cosine similarity between the vectors that represent the word embeddings of the words in the nodes. Testing Minimum Word Frequency presented a different problem than most of the other parameter tests. By setting a threshold on frequency, it would be possible for a tweet to be comprised entirely of words that would not exist in the vocabulary of the vector sets. With the scalar comparison formulas dependent on the cosine similarity of a term and the search term, if a vector did not exist, it is possible for some of the tweets to end up with component elements in the denominator equal to zero.

Recall accuracy

In particular, whereas each Asian language was studied mainly in its respective country, research about English was popular across all Asian countries. Moreover, topics about ‘discourse’ (i.e., ‘conversation analysis,’ ‘critical discourse analysis,’ ‘discourse analysis’) and ‘language education’ (i.e., ‘higher education,’ ‘language learning,’ ‘second language acquisition’) were likewise prominent. Given the increasing interest in computerized language analyses, ‘sentiment analysis’ was another hot topic. Another stream of research is concerned with how ‘language and linguistics’ research has been carried out in specific regions. For instance, Ngoc and Barrot (2022) conducts a bibliometric study concentrated on ‘English Language Teaching (ELT)’, a popular topic in ‘language and linguistics’ research. Unlike other studies covering a specific topic without any regional boundaries, the study focused on how ELT-related research has been conducted in ten Southeast Asian countries (Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, and Viet Nam).

There is also research investigating the meaning patterns of the construction in terms of the VP slot (cf. Zhan, 1998; Wang, 2002) and the NP slot (cf. Shen and Wang, 2000). It is still unknown what typical meanings of the NP slot and the VP slot denote respectively, leading further to the uncertainty of the meaning patterns of the NP de VP construction because construction itself is meaningful (cf. Goldberg, 1995, 2006). The NP de VP construction in modern Chinese has been of constant interest to theoretical linguists. Previous studies foregrounded word classes of lexical items in the VP slot, semantic relationships between the ChatGPT App VP and the NP, and lexical items that could fill both the VP and the NP slots of the construction. However, in this research, we highlighted typical meanings of this construction and the denoted meaning patterns of lexical items in both the VP and the NP slots by drawing on the covarying collexeme analysis and the hierarchical cluster analysis, respectively. Empirical studies have demonstrated how alternative methods based on textual analysis are more reliable and could complement and reduce the limitations of survey-based methods to describe current economic conditions and better predict a household’s future economic activity.

The presence of meaning refers to the subjective sense that one’s life is meaningful whereas the search for meaning implies the drive and orientation toward finding meaning in one’s life, both of which are significant to one’s well-being and personal achievement (Dezutter et al., 2013). As Frankl (1992) argued, human beings are characterized by a “will to meaning,” characterized as the forceful drive to search for meaning and significance in their life, and failing to achieve meaning can lead to psychological distress. Additionally, Maslow (1971) also stated in his theory of a hierarchy of needs that meaning in life is important for individuals to maximize their full potential and attain self-fulfillment.

This might suggest that individual differences may be stronger with some aspects of semantic processing than others, and that they may be able to be better isolated with ERPs than simple behavioral measures. Unfortunately, however, the analyses of Yap et al. and Stoltz et al. were done on lexical decision and not naming data so they are not as comparable as they might otherwise be to the data set here. The reason this study investigated such rank shifts first is that they tend to occur in English–Chinese translation when we study the transitivity shifts, for the two languages’ different habits of expressing one thing bring out the inclination to use different rank scales.

Since abstract words are less imaginable they might additionally activate the anterior temporal lobe in the phase of early detection of the word category. It is important to note that the anterior temporal lobe is presumed to have a graded specialization where the superior part is predicted to be more active for abstract and the ventromedial part for concrete words. The spatial resolution of this study does, unfortunately, not allow for more fine-grained distinctions within the anterior temporal lobe101. This was the sole connection exhibiting a larger strength of connectivity for abstract word reading, as all remaining differences exerted a stronger connection during the reading of concrete words in the alpha and beta bands in a later time window.

In Analysis 4, we test whether the word representations in the to-be-learned pair change asymmetrically. In this analysis, we correlate the representation of the cue word before learning with that of the target word after learning and the representation of the cue word after learning with that of the target word before learning. While differences in recall accuracy based on semantic relatedness and learning condition show that these factors are consequential for memory, they cannot show how this happens.