The Sociology of Interpretation

Semantic association computation is the process of quantifying the strength of a semantic connection between two textual units, based on different types of semantic relations. Semantic association computation is a key component of various applications belonging to a multitude of fields, such as computational linguistics, cognitive psychology, information retrieval and artificial intelligence. The field of semantic association computation has been studied for decades. The aim of this paper is to present a comprehensive survey of various approaches for computing semantic associations, categorized according to their underlying sources of background knowledge. Existing surveys on semantic computation have focused on a specific aspect of semantic associations, such as utilizing distributional semantics in association computation or types of spatial models of semantic associations. However, this paper has put a multitude of computational aspects and factors in one picture. This makes the article worth reading for those researchers who want to start off in the field of semantic associations computation. This paper introduces the fundamental elements of the association computation process, evaluation methodologies and pervasiveness of semantic measures in a variety of fields, relying on natural language semantics. Along the way, there is a detailed discussion on the main categories of background knowledge sources, classified as formal and informal knowledge sources, and the underlying design models, such as spatial, combinatorial and network models, that are used in the association computation process. The paper classifies existing approaches of semantic association computation into two broad categories, based on their utilization of background knowledge sources: knowledge-rich approaches; and knowledge-lean approaches. Each category is divided further into sub-categories, according to the type of underlying knowledge sources and design models of semantic association. A comparative analysis of strengths and limitations of various approaches belonging to each research stream is also presented. The paper concludes the survey by analyzing the pivotal factors that affect the performance of semantic association measures.

The development of information technology has encouraged the widespread adoption of technology-related loanwords in the Indonesian language, particularly in digital communication. The intensive use of these loanwords across diverse contexts has led to semantic shifts in their meanings. This study aims to analyze semantic shifts in technology-related loanwords in Indonesian by employing word embedding techniques as a computational linguistic approach. The research adopts a quantitative method based on the analysis of Indonesian digital text corpora representing language use across different time periods. The data are analyzed through text preprocessing, word embedding modeling, and the measurement of semantic relationship changes using cosine similarity and nearest-neighbor analysis. The results indicate that several technology-related loanwords have undergone significant semantic shifts, including meaning expansion, changes in semantic associations, and shifts in usage functions in line with technological developments and digital culture. These findings demonstrate that word embedding techniques are effective for empirically detecting semantic shifts in Indonesian. This study is expected to contribute methodologically to Indonesian semantic studies and to enrich corpus-based computational linguistics research.

The aim of the current study is to enhance our understanding of cognitive creativity, specifically divergent thinking, by employing an interdisciplinary methodological approach. By integrating methodology from computational linguistics and complex systems into creativity research, the current study aims to shed light on the relationship between divergent thinking and the temporal structure of semantic associations. In complex systems, temporal structures can be described on a continuum from random to flexible-stable and to persistent. Random structures are highly unpredictable, persistent structures are highly predictable, and flexible-stable structures are in-between, they are partly predictable from previous observations. Temporal structures of associations that are random (e.g., dog–graveyard–north pole) or persistent (e.g., dog–cat–rat) are hypothesized to be detrimental to divergent thinking. However, a flexible-stable structure (e.g., dog–police–drugs) is hypothesized to be related to enhanced divergent thinking (inverted-U). This notion was tested (N = 59) in an association chain task, combined with a frequently used measure of divergent thinking (i.e., Alternative Uses Test). Latent Semantic Analysis from computational linguistics was used to quantify the associations, and methods from complex systems in form of Power Spectral Density analysis and detrended fluctuation analysis were used to estimate the temporal structure of those associations. Although the current study does not confirm that a flexible-stable (vs. random/persistent) temporal structure of associations is related to enhanced divergent thinking skills, it hopefully challenges fellow researchers to refine the recent methodological developments for assessing the (temporal) structure of associations. Moreover, the current cross-fertilization of methodological approaches may inspire creativity researchers to take advantage of other fields’ ideas and methods. To derive a theoretically sound cognitive theory of creativity, it is important to integrate research ideas and empirical methods from a variety of disciplines.

Is my idea creative? This question directs investing in companies and choosing a research agenda. Following previous research, we focus on the originality of ideas and consider their association with self-assessments of idea generators regarding their own originality. We operationalize the originality score as the frequency (%) of each idea within a sample of participants and originality judgment as the self-assessment of this frequency. Initial evidence suggests that originality scores and originality judgments are produced by separate processes. As a result, originality judgments are prone to biases. So far, heuristic cues that lead to such biases are hardly known. We used methods from computational linguistics to examine the semantic distance as a potential heuristic cue underlying originality judgments. We examined the extent to which the semantic distance would contribute additional explanatory value in predicting originality scores and originality judgments, above and beyond cues known from previous research. In Experiment 1, we re-analyzed previous data that compared originality scores and originality judgments after adding the semantic distance of the generated ideas from the stimuli. We found that the semantic distance contributed to the gap between originality scores and originality judgments. In Experiment 2, we manipulated the examples given in task instructions to prime participants with two levels of idea originality and two levels of semantic distance. We replicated Experiment 1 in finding the semantic distance as a biasing factor for originality judgments. In addition, we found differences among the conditions in the extent of the bias. This study highlights the semantic distance as an unacknowledged metacognitive cue and demonstrates its biasing power for originality judgments.

Automatic associations involving alcohol have been proposed to play a role in drinking behavior. Such associations are often assessed using implicit measures such as the Implicit Association Test (IAT). Neural network language models provide computational measures of semantic relationships between words. These model-based measures could be related to behavioral alcohol-related associations as observed using the IAT. If so, this could provide a step toward better understanding of the nature of automatic associations and their relationship to behavior. The current study therefore aimed to test whether there is a systematic covariation over items between model-based and behavior-based associations. Analyses were performed for two single-target IATs from a previously published study. One task involved alcohol versus nonalcohol drinks and positive associates, and the other alcohol versus nonalcohol drinks and negative associates. The GenSim library and a pretrained word2vec model were used to calculate a relative computational association between specific items from the positive and negative categories, respectively, and the alcohol versus nonalcohol word sets. In both tasks, a significant covariance between items’ computational and behavioral measures of association was found over participants. The results thus add to the information on the relationship between neural network language models and psychological associations. They may provide methodological strategies for task design and data analysis. Models of semantic associations connect computational linguistics and social-cognitive psychology and may provide a theoretical link between measures of alcohol-related associations using verbal stimuli and alcohol-related cognition and behaviors.