scholarly journals Semantic network analysis of abstract and concrete word associations

2021 ◽  
Author(s):  
Dounia Lakhzoum ◽  
IZAUTE ◽  
Ludovic FERRAND
Keyword(s):  
Word Association ◽  
Semantic Network ◽  
Small World ◽  
Concrete Word ◽  
Clustering Coefficient ◽  
Structural Topology ◽  
Abstract Concepts ◽  
Word Associations ◽  
Network Analyses ◽  
Concrete Concepts

In recent years, a new interest for the use of graph-theory based networks has emerged within the field of cognitive science. This has played a key role in mining the large amount of data generated by word association norms. In the present work, we applied semantic network analyses to explore norms of French word associations for concrete and abstract concepts (Lakhzoum et al., 2021). Graph analyses have shown that the network exhibits high clustering coefficient, sparse density, and small average shortest path length for both the concrete and abstract networks. These characteristics are consistent with a small-world structure. Comparisons between local node statistics and global structural topology showed that abstract and concrete concepts present a similar local connectivity but different overall patterns of structural organisation with concrete concepts presenting an organisation in densely connected communities compared to abstract concepts. These patterns confirm previously acquired knowledge about the dichotomy of abstract and concrete concepts on a larger scale. To the best of our knowledge, this is the first attempt to confirm the generalisability of these properties to the French language and with an emphasis on abstract and concrete concepts.

scholarly journals COMPLEX STRUCTURES AND SEMANTICS IN FREE WORD ASSOCIATION

2012 ◽  
Vol 15 (03n04) ◽  
pp. 1250054 ◽  
Author(s):  
PIETRO GRAVINO ◽  
VITO D. P. SERVEDIO ◽  
ALAIN BARRAT ◽  
VITTORIO LORETO
Keyword(s):  
Human Brain ◽  
Word Association ◽  
Semantic Network ◽  
Free Association ◽  
Small World ◽  
South Florida ◽  
Characteristic Scale ◽  
Property A ◽  
Word Associations ◽  
Free Word

We investigate the directed and weighted complex network of free word associations in which players write a word in response to another word given as input. We analyze in details two large datasets resulting from two very different experiments: On the one hand the massive multiplayer web-based Word Association Game known as Human Brain Cloud, and on the other hand the South Florida Free Association Norms experiment. In both cases, the networks of associations exhibit quite robust properties like the small world property, a slight assortativity and a strong asymmetry between in-degree and out-degree distributions. A particularly interesting result concerns the existence of a characteristic scale for the word association process, arguably related to specific conceptual contexts for each word. After mapping, the Human Brain Cloud network onto the WordNet semantics network, we point out the basic cognitive mechanisms underlying word associations when they are represented as paths in an underlying semantic network. We derive in particular an expression describing the growth of the HBC graph and we highlight the existence of a characteristic scale for the word association process.

scholarly journals How well do similarity measures predict priming in abstract and concrete concepts?

2018 ◽  
Author(s):  
Maria Montefinese ◽  
Erin Michelle Buchanan ◽  
David Vinson
Keyword(s):  
Word Association ◽  
Semantic Representation ◽  
Similarity Measures ◽  
Semantic Features ◽  
Association Strength ◽  
Abstract Concepts ◽  
Strategic Processes ◽  
Different Types ◽  
Two Measures ◽  
Concrete Concepts

Models of semantic representation predict that automatic priming is determined by associative and co-occurrence relations (i.e., spreading activation accounts), or to similarity in words' semantic features (i.e., featural models). Although, these three factors are correlated in characterizing semantic representation, they seem to tap different aspects of meaning. We designed two lexical decision experiments to dissociate these three different types of meaning similarity. For unmasked primes, we observed priming only due to association strength and not the other two measures; and no evidence for differences in priming for concrete and abstract concepts. For masked primes there was no priming regardless of the semantic relation. These results challenge theoretical accounts of automatic priming. Rather, they are in line with the idea that priming may be due to participants’ controlled strategic processes. These results provide important insight about the nature of priming and how association strength, as determined from word-association norms, relates to the nature of semantic representation.

scholarly journals Directed random geometric graphs

2019 ◽  
Vol 7 (5) ◽  
pp. 792-816
Author(s):  
Jesse Michel ◽  
Sushruth Reddy ◽  
Rikhav Shah ◽  
Sandeep Silwal ◽  
Ramis Movassagh
Keyword(s):  
Real World ◽  
Word Association ◽  
Graph Model ◽  
Small World ◽  
Geometric Graph ◽  
Clustering Coefficient ◽  
Random Geometric Graph ◽  
Random Geometric Graphs ◽  
Scale Free ◽  
Small World Property

Abstract Many real-world networks are intrinsically directed. Such networks include activation of genes, hyperlinks on the internet and the network of followers on Twitter among many others. The challenge, however, is to create a network model that has many of the properties of real-world networks such as power-law degree distributions and the small-world property. To meet these challenges, we introduce the Directed Random Geometric Graph (DRGG) model, which is an extension of the random geometric graph model. We prove that it is scale-free with respect to the indegree distribution, has binomial outdegree distribution, has a high clustering coefficient, has few edges and is likely small-world. These are some of the main features of aforementioned real-world networks. We also empirically observed that word association networks have many of the theoretical properties of the DRGG model.

scholarly journals The Small World of Words: English word association norms for over 12,000 cue words

2019 ◽  
Author(s):  
Simon De Deyne ◽  
Danielle Navarro ◽  
Amy Perfors ◽  
Marc Brysbaert ◽  
Gert Storms
Keyword(s):  
English Language ◽  
Word Association ◽  
Small World ◽  
English Word ◽  
Spreading Activation ◽  
Word Associations ◽  
Semantic Categorization ◽  
Language Resource ◽  
The World ◽  
Cue Words

Word associations have been used widely in psychology, but the validity of their application strongly depends on the number of cues included in the study and the extent to which they probe all associations known by an individual. In this work, we address both issues by introducing a new English word association dataset. We describe the collection of word associations for over 12,000 cue words, currently the largest such English-language resource in the world. Our procedure allowed subjects to provide multiple responses for each cue, which permits us to measure weak associations. We evaluate the utility of the dataset in several different contexts, including lexical decision and semantic categorization. We also show that measures based on a mechanism of spreading activation derived from this new resource are highly predictive of direct judgments of similarity. Finally, a comparison with existing English word association sets further highlights systematic improvements provided through these new norms.

Effects of Context on Processing Emotionally Neutral Abstract and Concrete Concepts

2016 ◽  
Vol 44 (7) ◽  
pp. 1191-1200 ◽  
Author(s):  
Liusheng Wang ◽  
Hongmei Qiu ◽  
Jianjun Yin
Keyword(s):  
College Students ◽  
Lexical Decision ◽  
Lexical Decision Task ◽  
Emotional Valence ◽  
Decision Task ◽  
Abstract Concepts ◽  
Context Condition ◽  
Word Context ◽  
Concrete Concepts ◽  
Concrete Words

The abstractness effect describes the phenomenon of individuals processing abstract concepts faster and more accurately than they process concrete concepts. In this study, we explored the effects of context on how 43 college students processed words, controlling for the emotional valence of the words. The participants performed a lexical decision task in which they were shown individual abstract and concrete words, or abstract and concrete words embedded in sentences. The results showed that in the word-context condition the participants' processing of concrete concepts improved, whereas in the sentence-context condition their processing of abstract concepts improved. These findings support the embodied cognition theory of concept processing.

scholarly journals Students’ Word Associations with Different Terms Related to the Wadden Sea: Does the Place of Residence (Coast or Inland) Have an Influence?

2021 ◽  
Vol 11 (6) ◽  
pp. 284
Author(s):  
Till Schmäing ◽  
Norbert Grotjohann
Keyword(s):  
Wadden Sea ◽  
Word Association ◽  
Place Of Residence ◽  
Word Associations ◽  
Quantitative Content ◽  
Visitation Frequency ◽  
Written Form ◽  
Free Word ◽  
Different Levels ◽  
Out Of School

This paper presents students’ word associations with terms regarding the Wadden Sea. A continuous free word-association method was used in which the students from secondary schools (n = 3119, average age: 13.54 years) reported their associations with the stimulus words Wadden Sea, mudflat hiking tour, and tides in written form. Data were collected from students living close to the Wadden Sea and from students living inland. We performed a quantitative content analysis including the corresponding formation of categories. In addition, students’ school, out-of-school with the class, and private experiences the Wadden Sea ecosystem were recorded. The study shows that not only subject-related concepts should be considered at different levels, but non-subject-related aspects as well. The associations of the inland and non-inland students are statistically significantly different. The Wadden Sea and its biome were found to be completely unknown to some students. Students’ school, out-of-school with the class, and private experiences of the wetlands are also very mixed, regarding their Wadden Sea visitation frequency, and surprisingly cannot be directly derived from their place of residence. This research makes an important contribution towards the design of future biology didactic studies on the Wadden Sea.

scholarly journals The Communicative Constitution of Hate Organizations Online: A Semantic Network Analysis of “Make America Great Again”

2018 ◽  
Vol 4 (3) ◽  
pp. 205630511879076 ◽  
Author(s):  
Sean M. Eddington
Keyword(s):  
Semantic Network ◽  
The United States ◽  
Political Organization ◽  
White Supremacist ◽  
Far Right ◽  
Network Analyses ◽  
The United Kingdom ◽  
White Supremacists ◽  
Media Systems ◽  
Communicative Constitution

In the context of the 2016 U.S. Presidential Election, President Donald Trump’s use of Twitter to connect with followers and supporters created unprecedented access to Trump’s online political campaign. In using the campaign slogan, “Make America Great Again” (or its acronym “MAGA”), Trump communicatively organized and controlled media systems by offering his followers an opportunity to connect with his campaign through the discursive hashtag. In effect, the strategic use of these networks over time communicatively constituted an effective and winning political organization; however, Trump’s political organization was not without connections to far-right and hate groups that coalesced in and around the hashtag. Semantic network analyses uncovered how the textual nature of #MAGA organized connections between hashtags, and, in doing so, exposed connections to overtly White supremacist groups within the United States and the United Kingdom throughout late November 2016. Cluster analyses further uncovered semantic connections to White supremacist and White nationalist groups throughout the hashtag networks connected to the central slogan of Trump’s presidential campaign. Theoretically, these findings contribute to the ways in which hashtag networks show how Trump’s support developed and united around particular organizing processes and White nationalist language, and provide insights into how these networks discursively create and connect White supremacists’ organizations to Trump’s campaign.

Do word associations assess word knowledge? A comparison of L1 and L2, child and adult word associations

2010 ◽  
Vol 15 (2) ◽  
pp. 187-204 ◽  
Author(s):  
Marjolein Cremer ◽  
Daphne Dingshoff ◽  
Meike de Beer ◽  
Rob Schoonen
Keyword(s):  
Conceptual Development ◽  
Assessment Tool ◽  
Word Association ◽  
Word Knowledge ◽  
Word Associations ◽  
Bilingual Speakers ◽  
Association Behavior ◽  
Log Linear ◽  
L1 And L2 ◽  
Free Word

Differences in word associations between monolingual and bilingual speakers of Dutch can reflect differences in how well seemingly familiar words are known. In this (exploratory) study mono-and bilingual, child and adult free word associations were compared. Responses of children and of monolingual speakers were found to be more dispersed across response categories than responses of adults and of L2 speakers, respectively. Log linear analyses show that the distributional patterns of association responses differ among the groups. Age has the largest effect on association responses. Adults give more meaning-related responses than children. Child L1 speakers give more meaning-related responses than child L2 speakers. Form-based and ‘Other’ associations were mostly given by (L2) children. The different findings for mono- and bilingual children and for mono- and bilingual adults show the influence of bilingualism on the development of word associations. The prominent effect of age emphasizes the role of conceptual development in word association behavior, and makes free word association tasks less suitable as an assessment tool for word knowledge.

CELLULAR NEURAL NETWORKS CAN MIMIC SMALL-WORLD NETWORKS

1999 ◽  
Vol 09 (10) ◽  
pp. 2105-2126 ◽  
Author(s):  
TAO YANG ◽  
LEON O. CHUA
Keyword(s):  
Neural Networks ◽  
Coupling Strength ◽  
Initial Conditions ◽  
Computing Time ◽  
Small World ◽  
Cellular Neural Networks ◽  
Clustering Coefficient ◽  
High Survival ◽  
Game Of Life ◽  
Hole Detection

Small-world phenomenon can occur in coupled dynamical systems which are highly clustered at a local level and yet strongly coupled at the global level. We show that cellular neural networks (CNN's) can exhibit "small-world phenomenon". We generalize the "characteristic path length" from previous works on "small-world phenomenon" into a "characteristic coupling strength" for measuring the average coupling strength of the outputs of CNN's. We also provide a simplified algorithm for calculating the "characteristic coupling strength" with a reasonable amount of computing time. We define a "clustering coefficient" and show how it can be calculated by a horizontal "hole detection" CNN, followed by a vertical "hole detection" CNN. Evolutions of the game-of-life CNN with different initial conditions are used to illustrate the emergence of a "small-world phenomenon". Our results show that the well-known game-of-life CNN is not a small-world network. However, generalized CNN life games whose individuals have strong mobility and high survival rate can exhibit small-world phenomenon in a robust way. Our simulations confirm the conjecture that a population with a strong mobility is more likely to qualify as a small world. CNN games whose individuals have weak mobility can also exhibit a small-world phenomenon under a proper choice of initial conditions. However, the resulting small worlds depend strongly on the initial conditions, and are therefore not robust.

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