A computational analysis of crosslinguistic regularity in semantic change

By far my favorite way to conduct exploratory data analyses on corpora is with topic models, and I have written multiple articles about how to go about this in the least painful way possible. While semantics analysis topic models are awesome, they are not universally the best method for all things text. With a small set of POS labels, the probability values for longer n-grams can be accurately estimated.

Ontologies describe concepts, relationships, and axioms that can be represented mathematically using symbolic notation. Some common mathematical representations and concepts used in ontology modeling are in Textbox 1. Ontologies written in Web Ontology Language (OWL) typically consist of several key components that define structures, classes, individuals, properties (data and object), axioms, restrictions, annotations, logical axioms, and namespaces.

ThoughtSource: A central hub for large language model reasoning data

The findings of this study have opened up other possible avenues of research to pursue. While conducting our analyses on the top keywords of Asian ‘language and linguistics’, because the keywords of more than 20% of the target articles were derived by deep learning-based BERT algorithm, this study evaluated the keywords ChatGPT without further intervention in any way. However, associations among keywords that could be derived from machine-learning-based topic modeling could yield other valuable results. For instance, the outcomes of topic modeling might allow one to group together several countries that have researched similar topics.

Through the use of semantic network analysis of online news, we conducted an investigation into consumer confidence. Our findings revealed that media communication significantly impacts consumers’ perceptions of the state of the economy. Figure 4 shows the economic-related keywords that can have a major role in influencing consumer confidence (those with the most significant Granger-causality scores, as presented in Section “Results”). As an example to begin with, we may consider two frequently discussed etyma in Indo-European linguistics, the two lexical roots for “wheel” (Carling, 2019, p. 345–54; Heggarty, 2014) (Table 3). Like in many of our reconstructions, most meanings end up at an intermediate probability (around 0.5).

3 Network stability and accuracy

Although Table 9 suggests that only about 14% of the fields of activity in ST were changed in their translations, this study found that where there is a shift in the field of activity, it is a process shift. In other words, when the original contextual field of activity is transformed in the TT, the process also tends to be changed to play different functions accordingly. The tendency of the shifts from other processes to material and relational process types is closely related to the genre of the text. The ACPP in most cases is quoted in ST and TT to justify, illustrate or emphasize the author’s political viewpoints and his philosophy of governance.

OWL ontologies can become more complex by adding multiple classes, properties, axioms, and imports, allowing formal representation and automated reasoning of complex knowledge structures. The studies involving humans were approved by the Ethics Committee of the Institute of Psychiatry and Neurology in Warsaw. The studies were conducted in accordance with the local legislation and institutional ChatGPT App requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

We took this semantic space as a common representation for operationalizing the similarity and analogy models. Another aspect of regularity pertains to the mapping between source meaning and target meaning in semantic change, or how new meanings are structured in relation to existing meanings of a word. Recent work has also shown that regular polysemy patterns hold crosslinguistically as they are examined in a synchronic, cross-sectional setting (Srinivasan and Rabagliati, 2015).

Chen (1987), Zhang (1993), Wang (2002, 2010), and Wu and Guo (2018) have examined the semantic relationship between the NP and the VP in the NP de VP construction. Concerning mubiao de shixian ‘realization of target’ in example (1), the NP mubiao ‘target’ functions as the patient of the VP shixian ‘realize’. There are also cases that the NP functions as the agent of the VP in the NP de VP construction; this could be exemplified by lingdao de tiba ‘promotion by leaders’ (Wang, 2002, p. 62), in which the NP lingdao ‘leaders’ functions as the agent of the VP tiba ‘promote’.

Defects caused by insufficient product conceptual design are difficult to be remedied in the manufacturing and maintenance stages. This stage starts from the customer requirements analysis, then gradually realizes the mapping from product functional to physical structure, and obtains the design scheme through evaluation and optimization in final2. Customer-centered product design philosophy is widely recognized by manufacturing enterprises nowadays. Therefore, narrowing the gap between product design and customer requirements is a pivotal goal from beginning to end. Previous published studies conduct customer investigations by questionnaire or interview to gather data for analyzing customer requirements. For the past few years, a large quantity of literature has researched the extraction of customer requirements from online comments3,4.

For abstract and concrete words, the total comes to respectively 251 ± 59 and 212 ± 47 trials per subject. In this study, we used event-related-potentials (ERPs) to examine the results of two different types of semantic priming. The first prime group used words semantically related and semantically unrelated to inconsistent and consistent targets words20. The idea is that, at least with low frequency words, inconsistent and consistent words typically produce different sized priming effects, with inconsistent words producing greater priming effects than consistent words. The second prime group used semantically unrelated and nonword primes, again with consistent and inconsistent target words.

An analysis of national media coverage of a parental leave reform investigating sentiment, semantics and contributors

Experiential meaning embodies the author’s or speaker’s understanding of the experience of the world, according to Halliday’s Systemic Functional Linguistics (SFL) (Halliday, 1994, Halliday and Matthiessen, 2004, 2014). This mode of meaning, as SFL theorists believed, carries basic information, and serves as the foundation for all kinds of texts to form their meanings, or more specifically, the metafunctional meanings inherent in language itself. Therefore, despite the principal difference between literary and non-literary texts, SFL’s experiential mode of meaning with its linguistic analytical approach to the transitivity system enables us to study how the ACPP was rendered in Xi’s representative works. Such an approach is thus of empirical significance for this research, to reveal the applicability of SFL in studying the translation of literary texts in non-literary texts, and offer practical guidance to translators rendering literary citations in political texts.

Evaluation of tissue by either histochemical stains or antigen-specific immunohistochemistry offers distinct and sometimes overlapping information, but both have limitations. Hematoxylin and eosin (H&E) staining is a rapid, reliable and inexpensive method; however, lack of molecular specificity and requirement for manual segmentation have, thus far, limited its use for extraction of quantifiable data. Consequently, disease assessments by H&E staining are typically qualitative and vulnerable to inter-observer variation and bias5,6,7.

Artificial Intelligence Versus the Data Engineer

Similarly, meaningfulness ratings in LLMs could be expected to have a similar relationship with phrase-level frequency, due to their training corpora. We tested this by examining Spearman’s correlations between human/LLM TWT meaningfulness ratings and logarithmic Google bigram frequency (Log_Gfreq) for each phrase, as provided in the original Graves dataset. Both human and LLM ratings were strongly correlated with Log_Gfreq (except for GPT-3.5, which had a weak but still statistically significant correlation; Table 4). First, it is one of the largest available sets of combinatorial noun-noun phrases that have corresponding human ratings.

In Section 3.2, it is noted that at a subsequence length of 2 (i.e., two microstates appearing in pairs), there is a significant increase in the probability of occurrence of the BA, BC, DA, DB, and DC sequences in SCZ patients. Conversely, the probability of occurrence of the CA and CB sequences decreases significantly. Furthermore, at a subsequence length of 3 (i.e., three microstates appearing simultaneously in a fixed order), SCZ patients exhibit the highest frequency of the ABA, BAB, BCB, and CBC subsequences, surpassing those observed in healthy subjects. These findings suggest the presence of specific subsequence patterns in the EEG signals of SCZ patients. The heightened occurrence of these subsequence patterns may, therefore, reflect abnormalities in speech processing, attention, and vigilance in individuals with SCZ.

As a result of Hummingbird, results are shortlisted based on the ‘semantic’ relevance of the keywords. You can foun additiona information about ai customer service and artificial intelligence and NLP. For example, semantic analysis can generate a repository of the most common customer inquiries and then decide how to address or respond to them. Additionally, we observe that in March 2022, the country with the highest similarity to Ukraine was Russia, and in April, it was Poland. In March, when the conflict broke out, media reports primarily focused on the warring parties, namely Russia and Ukraine. As the war continued, the impact of the war on Ukraine gradually became the focus of media coverage. For instance, the war led to the migration of a large number of Ukrainian citizens to nearby countries, among which Poland received the most citizens of Ukraine at that time.

• In our exploratory analysis, reported in supplementary material section D, we analyzed all consecutive time windows.
• 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’.
• As a result, they seem to have a deeper average semantic depth and a higher level of explicitness than verbs in ES.
• “We advise our clients to look there next since they typically need sentiment analysis as part of document ingestion and mining or the customer experience process,” Evelson says.
• On the other hand, all the syntactic subsumption features (ANPV, ANPS, and ARL) for A1 and A2 in CT are significantly lower in value than those in ES.

Doug Levy

Former USA Today reporter Doug Levy lives in New York City and spends a lot of his free time searching for great food and wine. As a PR pro, he specializes in healthcare and life sciences. Doug enjoys sharing his culinary observations which you can also read on his personal blog at Food and Wine World.