Semantic frame induction through the detection of communities of verbs and their arguments

Ribeiro, Edna; Teixeira, Andreia Sofia; Ribeiro, Ricardo; Matos, David Martins de

doi:10.1007/s41109-020-00312-z

Cited by 5 publications

(5 citation statements)

References 52 publications

(90 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A quality number of clusters to generate would be the number of clusters that optimizes the largest average silhouette width. Ribeiro et al [ 27 ] utilized maximum silhouette scores in their graph-clustering algorithm to identify groups of terms and their semantics. Their method, and the inclusion of silhouette scoring, outperformed previous methods.…”

Section: Methodsmentioning

confidence: 99%

Social media analytics of the Internet of Things

Scheibmeir

Malaiya

2021

Discov Internet Things

View full text Add to dashboard Cite

The Internet of Things technology offers convenience and innovation in areas such as smart homes and smart cities. Internet of Things solutions require careful management of devices and the risk mitigation of potential vulnerabilities within cyber-physical systems. The Internet of Things concept, its implementations, and applications are frequently discussed on social media platforms. This research illuminates the public view of the Internet of Things through a content-based and network analysis of contemporary conversations occurring on the Twitter platform. Tweets can be analyzed with machine learning methods to converge the volume and variety of conversations into predictive and descriptive models. We have reviewed 684,503 tweets collected in a 2-week period. Using supervised and unsupervised machine learning methods, we have identified trends within the realm of IoT and their interconnecting relationships between the most mentioned industries. We have identified characteristics of language sentiment which can help to predict the popularity of IoT conversation topics. We found the healthcare industry as the leading use case industry for IoT implementations. This is not surprising as the current COVID-19 pandemic is driving significant social media discussions. There was an alarming dearth of conversations towards cybersecurity. Recent breaches and ransomware events denote that organizations should spend more time communicating about risks and mitigations. Only 12% of the tweets relating to the Internet of Things contained any mention of topics such as encryption, vulnerabilities, or risk, among other cybersecurity-related terms. We propose an IoT Cybersecurity Communication Scorecard to help organizations benchmark the density and sentiment of their corporate communications regarding security against their specific industry.

show abstract

Section: Methodsmentioning

confidence: 99%

Social media analytics of the Internet of Things

Scheibmeir

Malaiya

2021

Discov Internet Things

View full text Add to dashboard Cite

show abstract

“…Second, we need an approach that allows us to estimate the extent of individual variations in lexical networks. Third, in educational applications simple enough methods for text analysis are needed because advanced methods of text analysis [8][9][10][11] require expertise and are thus not likely to be adopted.…”

Section: Introductionmentioning

confidence: 99%

Lexical Networks Constructed to Correspond to Students’ Short Written Responses: A Quantum Semantic Approach

Koponen

Södervik

Nousiainen

2023

Complex Networks and Their Applications XI

View full text Add to dashboard Cite

A simple method to construct lexical networks (lexicons) of how students use scientific terms in written texts is introduced. The method is based on a recently introduced quantum semantics generalization of a word-pair cooccurrence. Quantum semantics allows entangled co-occurrence, thus allowing to model the effect of subjective bias on weighting the importance of word cooccurrence. Using such a generalized word-pair co-occurrence counting, we construct students' lexicons of scientific (life-science) terms they use in their written responses to questions concerning food chains in life-science contexts. The method allows us to construct ensembles of lexicons that probabilistically simulate the variability of individual lexicons. The re-analyses of the written reports show that while sets of top-ranking terms contain nearly the same terms irrespective of details of the method used to count co-occurrences, the relative rankings of some key-terms may be different in quantum semantic analysis.

show abstract

“…Cognitive networks can identify semantic frames as communities of tightly interconnected concepts (Citraro & Rossetti, 2020; Correa & Amancio, 2019; Ribeiro, Teixeira, Ribeiro, & de Matos, 2020) or network neighborhoods (Stella, 2020), the latter being extensively used in psycholinguistic inquiries about language processing (cf. Vitevitch, 2019).…”

Section: Introductionmentioning

confidence: 99%

Cognitive Network Science for Understanding Online Social Cognitions: A Brief Review

Stella

2021

Topics in Cognitive Science

View full text Add to dashboard Cite

Social media are digitalizing massive amounts of users’ cognitions in terms of timelines and emotional content. Such Big Data opens unprecedented opportunities for investigating cognitive phenomena like perception, personality, and information diffusion but requires suitable interpretable frameworks. Since social media data come from users’ minds, worthy candidates for this challenge are cognitive networks, models of cognition giving structure to mental conceptual associations. This work outlines how cognitive network science can open new, quantitative ways for understanding cognition through online media like: (i) reconstructing how users semantically and emotionally frame events with contextual knowledge unavailable to machine learning, (ii) investigating conceptual salience/prominence through knowledge structure in social discourse; (iii) studying users’ personality traits like openness‐to‐experience, curiosity, and creativity through language in posts; (iv) bridging cognitive/emotional content and social dynamics via multilayer networks comparing the mindsets of influencers and followers. These advancements combine cognitive‐, network‐ and computer science to understand cognitive mechanisms in both digital and real‐world settings but come with limitations concerning representativeness, individual variability, and data integration. Such aspects are discussed along with the ethical implications of manipulating sociocognitive data. In the future, reading cognitions through networks and social media can expose cognitive biases amplified by online platforms and relevantly inform policy‐making, education, and markets about complex cognitive trends.

show abstract

Semantic frame induction through the detection of communities of verbs and their arguments

Cited by 5 publications

References 52 publications

Social media analytics of the Internet of Things

Social media analytics of the Internet of Things

Lexical Networks Constructed to Correspond to Students’ Short Written Responses: A Quantum Semantic Approach

Cognitive Network Science for Understanding Online Social Cognitions: A Brief Review

Contact Info

Product

Resources

About