Home » A computational analysis of crosslinguistic regularity in semantic change

A computational analysis of crosslinguistic regularity in semantic change

Meaning patterns of the NP de VP construction in modern Chinese: approaches of covarying collexeme analysis and hierarchical cluster analysis Humanities and Social Sciences Communications

semantics analysis

In an evolutionary reconstruction model, this matrix is schematically reorganized (Figure 2). We have access to observed feature data, consisting of polysemous meaning variants of lexemes in synchronic semantics analysis states. By means of a phylogenetic comparative model, we follow a procedure where we infer a model where an etymon can retain, gain or lose meanings with a certain probability at some time interval.

This analysis was initially attempted and resulted in no statistically significant results. However, this analysis assumes that the measured representation of each word in our set is independent from that of the other words in our set; in a neuroimaging-based representational similarity analysis (which our analysis was inspired by), this is indeed the case. However, in our paradigm, the semantic representation of each individual word is derived from its relationship to every other word in the set, and all of these words also underwent learning. Despite early theories that proposed a psychological and neurobiological separation between semantic and episodic memory systems1,2, there is an increasing body of work that suggests the two systems are more intertwined than previously believed3,4. Neuroimaging experiments have demonstrated shared neural activation5 and functional connectivity6,7 during episodic and semantic memory processes, and pre-existing semantic knowledge can act as a scaffold to facilitate the acquisition of new episodic memories8,9,10.

Social support represents the status that an individual is cared for, esteemed, and sustained by others or that one has material and psychological resources at one’s disposal (Taylor, 2011). One research found that for college students, support from important others, such as mothers and teachers, is a significant source of meaning in life (Li et al., 2022). Based on the structural equation model, Liu et al. (2022) suggested that a lack of social support during the pandemic may lead to enhanced feelings of loneliness and diminished perception of meaning in life. Et al. (2023) also stated that social support could increase college students’ optimism and then contribute to their feelings of meaning in life, which indicated the consistently promoting effect of social support even after the pandemic.

Similarity-based word arrangement task (SWAT)

As our study requires the collection of multiple trials per word type, models are needed that can account for the trial-to-trial variability59,60. As far as we know, the current state-of-the-art method of connectivity has not yet been applied to understand the patterns of word processing. In this study, we thus attempt to investigate the dynamic and directional connectivity patterns elicited during implicit processing of abstractness when reading single words. Here we consider regularity in semantic change to reflect recurring or predictable patterns in the historical shifts of word meaning, particularly as a new target meaning is derived from an existing source meaning over time.

semantics analysis

Source localization attempts to “unmix” the recordings to arrive at the location and the activation patterns of the underlying neural source. To capture true connectivity, we thus conducted our analysis at the source rather than at scalp level48. We defined our brain regions of interest (ROI) empirically to allow the inclusion of ROIs not predicted by current theories37. We also selected ROIs based on two distinct measures of neural activation in order both to distinguish between differences in connectivity and differences in activation, and to identify common and differential activation between abstract and concrete word comprehension. Among these methods and tools are neuroimaging techniques such as PET and fMRI which can assess the spatial activation of brain regions during concrete and abstract word processing. For example, one popular hypothesis is that the verbal and nonverbal systems are generally and respectively attributed to the left and right cerebral hemispheres9.

Part of the Latinx/Chicanx Cluster Hire Initiative, Phuong joined UC San Diego to be part of a community of scholars who are engaged in social justice efforts. In particular, he is eager to support the university’s efforts to become a Hispanic-Serving Institution. He brings a transformative framework that he co-developed at UC Berkeley called Adaptive Equity-Oriented Pedagogy that significantly improves student engagement, success and belonging.

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

This process is defined as isolating commonalities between words, determining a dimensional model capable of representing relationships between these words, and assigning numeric values to words based upon their individual spatial locations. This vectorization of words thus embeds meaning into these numerical representations. We have presented a large-scale computational analysis of shared regular patterns in semantic change.

Numbers of the dependent variable could be continuous (real numbers), discrete (integers), or both. It converts the table into a distance object by implementing an amalgamation rule (e.g., Ward’s method which, by employing an analysis of variance, evaluates the distances between clusters) which determines in what ways elements in the distance object could be clustered into groups. Selection of covarying collexeme analysis is directly motivated by the fact that it is an approach that testifies the probability of mutual prediction between the NP and the VP in the NP de VP construction. By so doing, we could easily identify instances that are significantly attracted to the NP slot and the VP slot in the construction. Drawing on these significantly attracted instances that could enter both the NP and the VP slots in the NP de VP construction, it is possible for us to further pattern the lexical items that are similar in meanings by means of the hierarchical cluster analysis.

Two models were proposed and they were the Presence-to-Search Model (people with low levels of presence of meaning will search for meaning) and the Search-to-Presence Model (people who search for meaning will experience greater meaning) (Steger et al., 2008). Thus, more studies are needed to explore the discrepancies and complex relation between search for meaning and presence of meaning. College years have long been seen as an important period for the adaptation and transformation into an independent and capable individual (Medalie, 1981).

For instance, when pairs of famous and novel faces are learned, multivariate neural representations of novel target faces are drawn towards those of their paired cue faces only when there is pre-existing knowledge about the cue face41. While this asymmetric representation is in the opposite direction to the one we observed in our data, it is important to note that in that study there was no pre-existing relationship between the paired faces and no prior knowledge surrounding the novel faces. In contrast, the word stimuli used in our study had a rich network of semantic associations prior to learning, with pre-existing semantic relationships between half of the pairs. It is possible that the assimilation of a target item representation into that of its paired cue item only occurs when existing semantic information about the cue can scaffold the integration of the novel information into the existing knowledge.

Then, we tested the LLMs on a binary version of the test (i.e., “makes sense”/“nonsense” judgment instead of numerical ratings) that was expected to be easier for LLMs. There are philosophical arguments as to why LLMs do not have true or humanlike understanding. For example, LLMs learn words-to-words mappings, but not words-to-world ChatGPT mappings, and hence cannot understand the objects or events that words refer to16. Such arguments aside, formal tests are critical, as that’s where “rubber meets the road.” If a system can match or surpass human performance in any task thrown at it, the argument that it does not possess real understanding rings hollow.

The different node sizes reflect the country’s ‘Degree’, which indicates the larger the node, the more different countries each corresponding country had collaborated with. The thickness of the line between countries represents the frequency of their collaborations. Briefly speaking, the United States, the United Kingdom, Australia, Canada, Germany, and The Netherlands all frequently collaborated with Asian countries to produce ‘language and linguistics’ research.

Other studies have shown an exceptional predominance of the occipito-temporal (OT) cortex in sending information41 and have, consistent with our findings, emphasized the importance of OT as the main entrance point from visual analysis to the language network. Furthermore, our study confirms that the medial, inferior and anterior temporal cortices are important for semantic processing, as previously suggested by Catani and Mesulam99. The main goal of our study was to investigate and compare the network dynamics of abstract and concrete word processing. Our results on the scalp-level revealed a centro-frontal difference in EEG amplitudes between abstract and concrete words starting from around 300 ms after the words were presented9,92. Having moved on to investigate differences on the source level, we found that visual word processing does not entail a simple bottom-up process but includes both bottom-up and top-down connections.

Media bias estimation by word embedding

Additionally, we noted in our pre-registration that we would exclude participants who reported rehearsing word pairs between sessions. The blue and red fonts represent the views of some “left-wing” and “right-wing” media outlets, respectively. In the era of information explosion, news media play a crucial role in delivering information to people and shaping their minds. Unfortunately, media bias, also called slanted news coverage, can heavily influence readers’ perceptions of news and result in a skewing of public opinion (Gentzkow et al. 2015; Puglisi and Snyder Jr, 2015b; Sunstein, 2002). The authors acknowledge University of Agder, Norway, to purchase MOX2 activity monitors. AC invited participants and handed over the MOX2-5 devices for anonymous activity data collection following the ethical guidelines and consent signing from Grimstad, Norway.

None of the predictor variables was perfect, and Table 1 shows examples of semantic change that were assigned with correct and incorrect directions by each of the variables. The semantic analysis method begins with a language-independent step of analyzing the set of words in the text to understand their meanings. This step is termed ‘lexical semantics‘ and refers to fetching the dictionary definition ChatGPT App for the words in the text. Each element is designated a grammatical role, and the whole structure is processed to cut down on any confusion caused by ambiguous words having multiple meanings. Semantic analysis analyzes the grammatical format of sentences, including the arrangement of words, phrases, and clauses, to determine relationships between independent terms in a specific context.

However, in spite of the progress, these methods often rely on manual observation and interpretation, thus inefficient and susceptible to human bias and errors. Media bias can be defined as the bias of journalists and news producers within the mass media in selecting and covering numerous events and stories (Gentzkow et al. 2015). This bias can manifest in various forms, such as event selection, tone, framing, and word choice (Hamborg et al. 2019; Puglisi and Snyder Jr, 2015b).

It predicts that any early semantic priming effects to do with low frequency inconsistent words should be correlated across tasks because the locus of the effects is the same. In this case, people who use early semantics when reading aloud on one task should have a very strong tendency to use early semantics on other reading tasks. With this study, this means that the size of the priming effect with inconsistent words when primed by related and unrelated words should be correlated with the size of the priming effect with inconsistent words when primed by unrelated and nonwords.

  • First, the current study uses a cross-sectional design, not allowing causal conclusions to be drawn.
  • 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).
  • Word embeddings are typically trained on large corpora so that they can capture general word-to-word relations in human language.
  • The relationship between the Perplexity-AverKL and the topic quantity is depicted in Fig.
  • All participants were Hispanics/Latinos from Colombia, self-identified as white in terms of race.

First, the tendency of the large proportion of shifts within the material clause is connected to the levels of delicacy. According to Halliday and Matthiessen (2004, p. 169–248), eight subtypes of material processes are used often (listed below), making three levels of delicacy. In this section, possible factors motivating process, participant and circumstance shifts will be discussed, including the consideration of specific contextual elements closely related to the choice of the transitivity system, namely, the register variable of field. In this example, the ST (literarily means the clearer the understanding, the more solid the practical action) is cited in a context where President Xi calls for officials at all levels to make efforts to learn five new development concepts to enable them to take root and become a common practice. While the first relational clause in the ST is nominalized into a phrase group, condensing information within the shift from a clause to a nominal group. The last typical meaning pattern that lexical items in the NP slot could be abstracted is “business” in that these items are concerned with various aspects of the business.

At this step, based on the characteristics of different types of media bias, we choose appropriate embedding methods to model them respectively (Deerwester et al. 1990; Le and Mikolov, 2014; Mikolov et al. 2013). Then, we utilize various methods, including cluster analysis (Lloyd, 1982; MacQueen, 1967), similarity calculation (Kusner et al. 2015), and semantic differential (Osgood et al. 1957), to extract media bias information from the obtained embedding models. The first is the inability to cover the fourth volume of Governance and its English translation released in July 2022.

Natural Language Processing markers in first episode psychosis and people at clinical high-risk

Other work has explored typological patterns in the lexicon (Kouteva et al., 2019; Thanasis et al., 2021) and taken a usage-based approach to account for the processes involved in language change (Bybee, 2015). Despite the similarity in emotionality of articles from left-oriented and right-oriented newspapers and male and female journalists, we found marked differences in language semantics. We showed a pronounced difference in the probabilities of the two topics occurring in articles written by female journalists compared to male journalists.

semantics analysis

Moreover, the sample contained a large portion of non-academic publications written for the public. As such, it is hard to apply the results from the bibliometric analyses of academic articles, as the current study does. Keeping Asia’s linguistic diversity in mind, one may understandably surmise that, on the one hand, these 13 countries could have thoroughly investigated their own languages and sociolinguistic cultures.

The idea is that using nonwords and unrelated words provides an alternative baseline where the semantic effect of a nonword should be essentially zero if a long prime presentation is used, unlike unrelated words. In this case, any partial activation caused by a nonword being perceptually similar to other words should be minimized if enough time for word recognition is used. This group thus provides an alternative view of the time-course of semantic effects compared to the other group. Individual differences between the way people use the two routes with the Triangle model have also been proposed. Thus, if someone had a very efficient OtP route, the semantically mediated route would not be used much. Alternatively, semantic access would be used more by people who could not learn to read inconsistent words with their OtP route.

Each dot represents one article, while the boxplots and the distributions represent the spread of the estimates across categories. Female journalists included words from Topic 2 (which included words related to time and sharing) in their articles (left panel) more so than male journalists (right panel). For female journalists writing in left-oriented journals, the difference in topic used is particularly pronounced (top left-hand corner). This research suggests that improving the level of “SIA” (Self-acceptance) will do good to both social support (especially increasing use of support) and meaning in life. Interventions such as cognitive behavioral therapy and paint therapy group counseling can be implemented to improve self-acceptance among college students (Pasaribu and Zarfiel, 2018; Zheng et al., 2021). Second, the findings of this research imply that social support plays an important role in enhancing the meaning of life for college students.

(PDF) ‘Not’ in the Mood: the Syntax, Semantics and Pragmatics of Evaluative Negation. – ResearchGate

(PDF) ‘Not’ in the Mood: the Syntax, Semantics and Pragmatics of Evaluative Negation..

Posted: Wed, 06 Jan 2016 14:43:26 GMT [source]

By sticking to just three topics we’ve been denying ourselves the chance to get a more detailed and precise look at our data. The study involved 80 Spanish speakers from a well-characterized cohort7,28, including 40 early PD patients with varied cognitive profiles and 40 HCs. This sample size matches or surpasses that of previous PD studies using automated language tools8,20. All participants were Hispanics/Latinos from Colombia, self-identified as white in terms of race. No participant reported a multi-racial background nor indigenous, Asian, or African ancestry.

However, due to insufficient time for collecting the citation information of relevant articles, it was premature for the current study to measure the impact of these topics brought about by Asian ‘language and linguistics’ research. Therefore, it will be an imperative academic path to take, to analyze the research trends of computerized language analyses in Asian ‘language and linguistics’ research. Once the gain and loss probabilities are known, the probability of a certain meaning at hidden nodes and the root is calculated from meanings of the leaf nodes using the peeling algorithm (Felsenstein, 2004, p. 253–54). The model excluded loans and runs the model for all 1,165 etymological trees in the dataset. The resulting trees have probabilities of presence of all meanings at hidden nodes of the trees. The original data contained a coding of the semantic relation between the concept meaning and the colexified meanings of lexemes in etyma (see Section 2.3).

There are 70,750 reconstructed meaning probabilities (ranging from 0 to 1) at 86 ancestral nodes (Supplementary Table S3) inside etymological trees. The computation made use of Glottolog trees, and therefore the naming of ancestral nodes follows the Glottolog standard. The folder (Supplementary Table S9) gives all reconstructed etymological trees, including probabilities for each meaning at the root and at attested stages (but not at intermediate nodes, this information is given in Supplementary Table S2). You can foun additiona information about ai customer service and artificial intelligence and NLP. Meanings with a probability larger than 0.75 are marked by green in the reconstruction (Supplementary Table S9). We are aware that the decision to include etymologies of changed meaning may give rise to inconsistencies and impact the results. However, we also believe that including this coding from the original data may give a more interesting result on semantic evolution.

It is important to note that the questions do not refer to specific modules but aim to assess the general perception of the REDbox framework. CSUQ can be used with larger sample sizes (more than 100) and smaller ones (fewer than 15). Despite the difference in precision, according to Tullis and Stetson, a sample size of 12 generates the same results as a larger sample size 90% of the time41. Yet, small samples are typically seen in usability and satisfaction tests and are generally sufficient for usability evaluations42,43. Finally, using additional tools provided by the Data Quality module (validation rules, calendar, alerts), the research project team can manage the data and follow the project during the research lifecycle.

Uber uses semantic analysis to analyze users’ satisfaction or dissatisfaction levels via social listening. This implies that whenever Uber releases an update or introduces new features via a new app version, the mobility service provider keeps track of social networks to understand user reviews and feelings on the latest app release. Upon parsing, the analysis then proceeds to the interpretation step, which is critical for artificial intelligence algorithms. For example, the word ‘Blackberry’ could refer to a fruit, a company, or its products, along with several other meanings. Moreover, context is equally important while processing the language, as it takes into account the environment of the sentence and then attributes the correct meaning to it. Individual words were pseudo-randomly assigned to trials based on the to-be-learned pairs.

semantics analysis

A natural way to explore semantic representations of documents is to project them into lower dimensional spaces (usually 2D) and use these projections for visualizing the documents. I chose all-MiniLM-L6-v2 as it is very stable, widely used and is quite small, so it will probably run smoothly even on your personal computer. Well this could go a long way, but the problem is that we completely lose all information about the importance of different words, their order in the sentence and all contextual information as well. Individually investigating every words’ relation to other words becomes tedious very quickly though.

The lack of difference in sentiment between newspapers with different orientations is striking, given that the political parties were almost evenly split in their support of the earmarking of 11 weeks of leave to fathers27. (A) The estimated difference in sentiment between male journalists and female journalists (top-left panel) and left-oriented and right-oriented newspapers (top-right panel) in news articles about the parental leave reform. (B) The estimated difference in sentiment between articles reporting on parental leave compared with “General News” control articles, independent of journalist gender or political orientation of the newspaper. Values below 0 indicate a higher likelihood for a given sentiment in the parental reform news, while values above 0 indicate a higher likelihood in the “General News”.

To avoid double-counting of transitivity shift types, the sample size should be 310 items with 305 translations. Concerning the analytical unit of clause for the transitivity system, there are 890 clauses, including the ranking clause (independent and subordinate clauses) and embedded clauses (functioning as participants despite their clause-like structure) in the ST and 824 clauses in the TT. What this study considered in terms of the register of the factors motivating transitivity shifts is the field variable, more specifically, the fields of activity directly linked to the reproduction of experiential meaning. Matthiessen (2015, p. 55–56), developed eight main fields of activity (see Figure 2) to describe the nature of the activity that comprises the situation.

semantics analysis

If the performance of this scoring mechanism proved to be nearly equivalent to others of the formulas, then it could be evaluated on the basis of resource and time consumption. If the neural network is only trained on all valid word-context pairs pairs in N, then any single pair has tremendous significance. The parameter for the negative sampling function, k, indicates a choice of k negative values that limits the impact of any single pair29,30.

If an LLM indeed lacks humanlike understanding, one ought to be able to design tests where it performs worse than humans. With such tests, the nebulous definition of “understanding” becomes less of a problem. It will take further work to understand, for example, whether dogs can generalise in the way humans learn to as infants, and grasp that the word “ball” need not refer to one specific, heavily chewed spongy sphere.

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