Reproducing experiential meaning in translation: A systemic functional linguistics analysis on translating ancient Chinese poetry and prose in political texts

A On each SWAT trial, a set of 60 words initially appeared in a random order in a box on the left side of the screen. Words would move to the main canvas when clicked, and once there could be dragged to a chosen location. Participants were instructed to place words that were more similar closer together but not given any rules for how to judge similarity. Participants could move words around until they were satisfied with their final arrangement. The assessment included four SWAT trials, and each word occurred on two of these trials. B Crucially, words from to-be-learned pairs never co-occurred on any SWAT trial to avoid potential contamination of their perceived relatedness, so the similarity of these pairs was imputed (see Methods).

It serves as a structured, common vocabulary or framework for describing concepts, entities, their properties, and their relationships within the domain. Physical activity data in MOX2-5 sensors were collected continuously, throughout the day with Bluetooth (BLE) short-range wireless technology standard at a fixed sampling rate, which is typically around 1 Hz (1 sample per second) and in the comma-separated-version (CSV) format. The data was typically sampled and recorded at very short intervals, often in real-time or near-real-time. The studies show thatmicrostate sequences are a valid electrophysiological marker for the identification of psychiatric classified disorders. First, this study used publicly available data instead of data obtained from cohort studies, and the number of subjects was small. Although age and gender were matched, it was difficult to obtain enough data to represent the general population.

On the other hand, the topic-word distribution of customer requirements is extracted without considering the behavioral and structural customer requirements. The reason is that the semantic presentation in the behavioral and structural domain is usually manifested as “verb-noun” matching form, which is difficult to be extracted directly. Where θmk represents the probability that topic zk appears in the document wm, φkv expresses the probability that word wv appears in the topic zk, nmk is the count of the document-topic and nkv is the count of the topic-word.

Meaning pattern of “augmentation”

This is the standard way to represent text data (in a document-term matrix, as shown in Figure 2). First, in line with the literature1, we predicted that patients in the overall cohort would be robustly identified through AT (but not nAT) retelling (i.e., via action semantic fields). Second, building on the previous work7,16, we hypothesized that such a selective AT pattern would be replicated in the PD-nMCI subgroup. Third, considering the same antecedents7,16, we anticipated that PD-MCI patients would be discriminated through semantic patterns in either text.

Zhan (1998) intended to identify certain types of verbs that could enter the VP slot of the construction, while this research highlights the typical meanings that verbs in the VP slot of the construction could denote. Differences in the purposes also render the different findings between this study and that conducted by Qi et al. (2004). In addition, we approached the same phenomenon with more delicacy than their study. That is to say, although all instantiated verbs in the VP slot identified by this study could be subsumed to those identified by Qi, et al. (2004), we further identified meaning patterns that verbs in the VP slot could denote by means of the hierarchical cluster analysis. When comparing the connectivity structure of reading between abstract and concrete words, a higher connection strength during abstract word processing was only observed in the alpha band during the 550–650 ms time window. This can be explained by the presence of increased attention at this time for abstract words100 and the fact that abstract, compared to concrete, words have a more pronounced linguistic component8.

Static training of machine learning systems on enormous corpora is effective for probabilistic interpretation of consistent meaning across a uniform body, but lacks the nuance necessary for interpreting polysemy as it changes from moment to moment. However, determining what tweets would be considered relevant to the needs of emergency personnel presents a more challenging problem. Challenging semantics coupled with different ways for using natural language in social media make it difficult for retrieving the most relevant set of data from any social media outlet. Tweets can contain any manner of content, be it observations of weather related phenomena, commentary on sports events, or social discussion. Isolating relevant tweets requires analysis of a multitude of characteristics such as location and time based metadata, but also the content of the tweet itself.

There are still some limitations and shortcomings in this work, which should be addressed in the future. On the one hand, the customer requirements acquired from the analogy-inspired VPA experiment are not abundant. More experiments are necessary to be implemented for providing massive and high-quality data.

How do we extract themes and topic from text using unsupervised learning

Lexical items that realize the former sense include jigou ‘organization’, tizhi ‘regulation’, tixi ‘system’, and jizhi ‘mechanism’, and those that realize the latter include jianli ‘establish’, sheli ‘set up’, and kaishe ‘set up’ (cf. rows 4 and 5 in Table 2). This pairing is evidenced by the example (3), in which the typical meaning regarding the pairing of “systems” and “establishment” in both slots of the NP de VP construction is realized by the significant covarying between tizhi ‘regulation’ and jianli ‘establish’. The next analysis was intended to investigate publication venues in which Asian ‘language and linguistics’ researchers were the most active in publishing their articles. Together, they published articles in 2349 different journals, and Table 2 shows the top 20 journals.

It may be unsurprising that a large number of Poles, Estonians, and Swedes – the Justice camp members – support greater defence spending. But it is striking to see that Germany – whose national identity was defined by pacificism for eight decades – is one of the few other countries where a plurality of the public backs more defence spending. But in most other countries, the prevailing view (and the majority in Italy, Greece, Spain, and Switzerland) is that their country should not be spending more on defence, despite the war. If Russia’s invasion was a wake-up call for Europe, it appears that waking up is not the same as getting out of bed. At the same time, many European citizens support the involvement of national troops in other ways, such as by providing technical assistance to the Ukrainian military or patrolling the Ukraine-Belarus border. This scepticism does not dramatically decrease even when respondents are asked to imagine what would happen if Ukraine receives increased weapons supplies.

Additionally, Table 7 shows each country’s total number of international citations, the average international citations per article, and the top five countries for the most often-cited the corresponding country’s publications. 12 presents the nodes corresponding to each of the target 13 countries, and the size of each node (i.e., each country) varies depending on the country’s ‘in-degree’. The larger the node, the higher number of diverse countries that took advantage of the node country’s articles as references. The thickness of the arrow coming into each semantics analysis node represents how many times the corresponding country was cited by the countries from which each arrow originated. Before delving into the specific details of the productivity of ‘language and linguistics’ research in our 13 target countries, this study first inquired into the contributing portions of these countries’ articles about the field, compared to articles originating from other Asian countries. This was done to examine to what degree the research of these 13 countries is representative of Asian ‘language and linguistics’ research overall.

Feature-specific reaction times reveal a semanticisation of memories over time and with repeated remembering

Meanwhile, five kinds of customer requirements of elevator and corresponding keywords as well as their weight coefficients in the topic-word distribution are extracted. This work can provide a novel research perspective on customer requirements mining for product conceptual design through natural language processing. Compared to English, we had a more limited range of natural language processing models and tools for Danish35. We selected BERT Tone as it showed the best average performance across three sentiment benchmark datasets36.

You can foun additiona information about ai customer service and artificial intelligence and NLP. The empirical findings indicate that SBS ERK models produce the most accurate forecasts for Climate Overall, Personal, and Economic Climate, while adding sentiment leads to the best forecasting of Future Climate. To nowcast CCI indexes, we trained a neural network that took the BERT encoding of the current week and the last available CCI index score (of the previous month) as input. The network comprised a hidden layer with ReLU activation, a dropout layer for regularization, and an output layer with linear activation that predicts the CCI index.

As a matter of fact, treating the VP as nominalized items or verbs does not influence this research to achieve its purposes because we specifically highlight the meaning patterns of the VP and the NP in the NP de VP construction. Language is a critical means of expressing a country’s culture, national spirit, and national values (Sapir, 1929; Saussure, 1916; Tektigul et al., 2022). Each of our 13 target countries has its own language; even for those whose official language is English, other co-official and regional languages exist. Therefore, one can postulate that ‘language and linguistics’ studies from these 13 countries would address topics largely about the language(s) used in their own countries.

Unfortunately, most routing systems will send the email to an advisor who is an expert on the topic in the title and not on the topic in the body of the email, which is often the main issue the customer is reaching for. According to a 2020 survey by Seagate technology, ChatGPT around 68% of the unstructured and text data that flows into the top 1,500 global companies (surveyed) goes unattended and unused. With growing NLP and NLU solutions across industries, deriving insights from such unleveraged data will only add value to the enterprises.

• The early stages of pancreatic cancer evolution are well described in the mouse models8,9.
• As all causal factors need to be incorporated in the model, Granger Causality may produce misleading results when the true relationship involves more variables than those that have been selected107.
• In the Semantic Differential theory, a given object’s semantic attributes can be evaluated in multiple dimensions.

Metrics such as F1 score and Jaccard similarity score can be used for classification and measuring similarity, but they serve different purposes. The F1 score evaluates the performance of the classifier, and the Jaccard similarity score quantifies the similarity between sentences. Although they can be used together to evaluate classifier performance and the similarity between ChatGPT App predicted and actual label sets, they are not directly interchangeable. We used “Classifier F1-scores and their Jaccard similarities” to evaluate the quality of the generated synthetic data with “Table_evaluator” python library21. The Jaccard Similarity Score is a versatile and widely applicable metric that provides a simple and intuitive measure of similarity between sets.

Predictive accuracy of concreteness, frequency, and valence in inferring directionality of semantic change. “Combined” refers to the logistic regression model that combines the three variables. Under this scheme, we select target candidates randomly from the test set which is a random subset of the entire dataset, distinct from the training set that we use for model construction. We select from the test database instead of the entire database in case any of our models would give an advantage to senses that were already attested as a target in the train set. We denote semantic shift from sense si to sense sj as si → sj, when a word in some language had meaning si at some point in time but not meaning sj, and then evolved to take on meaning sj at a later point in time.

The suggested answer is unique but customised by the advisors and brings clarity and speed to advisors’ answers. Semantic analysis can detect the intention within the customer’s email and suggest one or several answers from a set of preformatted answers. Advisors can then select the suggested answer, modify, or adapt it if needed a send the response to the customer. A ‘search autocomplete‘ functionality is one such type that predicts what a user intends to search based on previously searched queries.

Even if exploratory in nature, our study suggests that news has important implications on consumer confidence during economic recessions, not only during an economic expansion, as suggested by recent research37. Overall, our models confirm the important role played by the media in shaping current judgments and future expectations11, and the impact that national and European politics have on shaping these assessments9. The result of phylogenetic comparative model, described in Section 2.4, consists first of reconstructed probabilities of presence (ranging from 0 to 1) of all lexemes at hidden nodes of all 1,165 etyma in our data (Supplementary Table S2).

Since senses are annotated as phrases with multiple words (such as “to calculate or count”), we estimated the concreteness, valence, frequency of these senses through the following process. We finally averaged the values of concreteness and valence variables for each phrase and took the average of the logarithmic frequency values because frequencies can be power-law distributed and thus highly skewed. Here we used the averaging method for calculating all the three predictors for simplicity and consistency, although we acknowledge that there might be other alternative methods. In semantic analysis, word sense disambiguation refers to an automated process of determining the sense or meaning of the word in a given context. As natural language consists of words with several meanings (polysemic), the objective here is to recognize the correct meaning based on its use. The semantic analysis process begins by studying and analyzing the dictionary definitions and meanings of individual words also referred to as lexical semantics.

4 and 5 demonstrate, some regions play a bigger role in sending or receiving information compared to others. Similarly, the left and right orbitofrontal gyri (ROI 2 and 10) send information mostly to the same regions except for the superior parietal lobules (ROI 8). Furthermore, two regions largely receiving information are the left anterior temporal lobe (ROI 3, receiving from almost all areas except from the right middle and superior temporal gyri) and the right middle and superior temporal gyri (ROI 9, receiving from all areas).

Functional connectivity measures such as GC belong to a branch of popular connectivity methods which include information on the directedness of information flow47 enabling scientists to estimate the temporal precedence of the influence of one variable in a system on another52,53. Additionally, as GC is a data-driven approach it does not assume predefined connections between variables (in our case ROIs). Apart from a few attempts54,55, the spatio-temporal patterns of interactions between brain areas on word processing has yet to be defined.

• AllenNLP, on the other hand, is a platform developed by Allen Institute for AI that offers multiple tools for accomplishing English natural language processing tasks.
• These scores are the raw cosine similarity, and have not been min-maxed for their relative time delta.
• The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
• Other work has suggested that prior knowledge plays a crucial role in the symmetry of concept representations after learning39,41,52.

In this case, there are process shifts from the relational clause to the material clause. The ancient Chinese essay is quoted to illustrate the importance of considering the realities and national conditions of China in choosing the path and system of the rule of law. In the ST, “事剧而功寡” means that if the ruler fails to base his work on national conditions, he will be busy, but the actions will remain fruitless. The original sentence consists of two relational-intensive-attributive clauses, characterizing the features of the two Carrier Participants “事” and “功” with the Attributes “剧” and “寡”.

This is also known as transfer learning, and has been a hot topic in machine learning for quite some time. Word embedding models are a set of approaches that learn latent vector representations of terms in an unsupervised fashion. When learning word embeddings from natural language, one essentially obtains a map of semantic relations in an embedding space. Roark et al. (2009) propose a method for separating between word vs. category prediction in the context of a hierarchy-sensitive probability models. Specifically, for the category predictions, the prefix probability of the context-word sequence omits from the probability of the generation of the word.

It depicts a series of multifactorial methods to examine the structure in data for the purpose of identifying clusters of similar objects (cf. Everitt et al., 2011; Desagulier, 2014). It agglomerates individuals based on their distances which rely on the parameters that characterize them. This approach has been widely used in linguistics, particularly in corpus linguistics (cf. Divjak and Gries, 2006, 2008; Gries and Stefanowitsch, 2010; Divjak and Fieller, 2014; Desagulier, 2017). Hierarchical cluster analysis requires the data be a table T of I observations or individuals and K variables.

It is possible, however, that too aggressive of a floor on occurrence frequency could diminish some of the nuanced meaning desired by this study. These papers focus largely on the use of social media as “sensors”, where individuals on the ground during crisis events can be leveraged to provide information. These individuals are not necessarily official responders, yet their information can be reliable when properly processed. 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.

In this cluster, there seem to be some other lexical items (e.g., lidu ‘force’, xingwei ‘behavior’, and nianling ‘age’) that are not directly related to the meaning pattern of “internal traits”. Those lexical items prompt the suggestion that hierarchical cluster analysis by means of referring to the covarying collexemes needs to be further improved, which is precluded from further discussion because of the purpose of this study. Concerning the covarying collexemes in the VP slot, corpus data reveal that these verbs by and large incorporate tigao ‘improve’, peiyang ‘cultivate’, and zengqiang ‘enhance’. This could be illustrated by the covarying collexemes zengqiang ‘enhance’ in (9a), peiyang ‘cultivate’ and tigao ‘improve’ in (9b), which significantly cooccur with nengli ‘ability’ in (9a), rencai ‘talent’ and sushi ‘quality’ in (9b), respectively in the NP slot of the NP de VP construction. Hierarchical cluster analysis is a member of cluster analysisFootnote 5 which is an umbrella term for a number of related agglomerating analyses.

The recordings were then filtered with the help of a 4th order Butterworth filter in the range of 0.5–30 Hz to discard low and high frequency noises. These parameter settings for multi-trial ERP data have been validated previously70,71. The recordings were epoched with a window between 200 ms pre- and 1000 ms post-stimulus intervals and baselined with the 200 ms pre-stimulus time-window. Based on visual inspection, recordings from bad channels of each subject were removed (36 ± 16 channels out of 128 per subject on average). Of the remaining channels, we then eliminated all trials whose maximum EEG amplitudes exceeded ±150 µV. As multivariate causality measures are generally sensitive to data pre-processing and are recommended only insofar as necessary to eliminate noise72, we opted to eliminate trials rather than apply an additional artefact reduction step.