Visualizing and Processing Information Not Uttered in Spoken Political and Journalistic Data: From Graphical Representations to Knowledge Graphs in an Interactive Application

Alexandris, Christina; Du, Jian; Floros, Vasilios

doi:10.1007/978-3-031-05409-9_16

Cited by 2 publications

(3 citation statements)

References 25 publications

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“…Crowd-sourced data resulted to new insights in the analysis and processing of information not uttered in spoken interaction and its integration in knowledge graphs (Alexandris, 2023), with its subsequent use in vectors and other forms of training data as dataset for training a neural network for Natural Language Processing (NLP) tasks. In the knowledge graphs generated by a (tested and evaluated) interactive application (Alexandris, Du, and Floros 2022), unspoken formation is represented by the "Context" relation and the distinctive nodes it connects. The "Context" relation connects nodes with information types implied by unspoken linguistic or paralinguistic features, co-occurring with the spoken word in the utterance.…”

Section: Integrating Implied and Unspoken Information In Knowledge Gr...mentioning

confidence: 99%

“…The integration of the Plutchik Wheel of Emotions targets in making more types of feelings, opinions, voices and reactions visible from more types of user groups (iii), especially where less-resourced languages (A) are concerned, but also users who are not very experienced in detecting/processing emotions, especially in a foreign language (B). The integration of the model in the knowledge graph generation application (Alexandris, Du, and Floros 2022) intends to result to overcoming barriers in text understanding (misunderstanding and misinterpretation) (Aspect 3) by minimizing the likelihood of errors / false assumptions (Aspect 6), especially from complex written/ spoken texts / text-types where emotion and/or opinion is expressed (Aspect 5). In other words, this application example (Alexandris, Du, and Floros 2022) with the employment of the Plutchick Wheel Model (Alexandris, 2023) from the discipline of Psychology targets to contribute to the resolution of complications involving the problematic Aspects 3, 5 and 6, with their compatibility to user requirements for Cases A and B and target (iii).…”

Section: Integrating Implied and Unspoken Information In Knowledge Gr...mentioning

confidence: 99%

“…The integration of the model in the knowledge graph generation application (Alexandris, Du, and Floros 2022) intends to result to overcoming barriers in text understanding (misunderstanding and misinterpretation) (Aspect 3) by minimizing the likelihood of errors / false assumptions (Aspect 6), especially from complex written/ spoken texts / text-types where emotion and/or opinion is expressed (Aspect 5). In other words, this application example (Alexandris, Du, and Floros 2022) with the employment of the Plutchick Wheel Model (Alexandris, 2023) from the discipline of Psychology targets to contribute to the resolution of complications involving the problematic Aspects 3, 5 and 6, with their compatibility to user requirements for Cases A and B and target (iii). With its integration in system design and/or seed/training data, approaches directly/indirectly linked to the Plutchick Wheel Model may contribute to NLP systems being more efficient and friendlier in assisting less experienced users in Sentiment Analysis and, also to produce better quality output for text types concerning less-resourced languages with complex information, emotion and opinion.…”

Section: Integrating Implied and Unspoken Information In Knowledge Gr...mentioning

confidence: 99%

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GenAI and Socially Responsible AI in Natural Language Processing Applications: A Linguistic Perspective

Alexandris

2024

AAAI-SS

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It is a widely-accepted fact that the processing of very large amounts of data with state-of-the-art Natural Language Processing (NLP) practices (i.e. Machine Learning –ML, language agnostic approaches) has resulted to a dramatic improvement in the speed and efficiency of systems and applications. However, these developments are accompanied with several challenges and difficulties that have been voiced within the last years. Specifically, in regard to NLP, evident improvement in the speed and efficiency of systems and applications with GenAI also entails some aspects that may be problematic, especially when particular text types, languages and/or user groups are concerned. State-of-the-art NLP approaches with automated processing of vast amounts of data in GenAI are related to observed problematic Aspects 1-7, namely: (1) Underrepresentation, (2) Standardization. These result to (3) Barriers in Text Understanding, (4) Discouragement of HCI Usage for Special Text Types and/or User Groups, (5) Barriers in Accessing Information, (6) Likelihood of Errors and False Assumptions and (7) Difficulties in Error Detection and Recovery. An additional problem are typical cases, such as less-resourced languages (A), less experienced users (B) and less agile users (C). A hybrid approach involving the re-introduction and integration of traditional concepts in state-of-the-art processing approaches, whether they are automatic or interactive, concerns the following targets: i), (ii) and (iii): Making more types of information accessible to more types of recipients and user groups (i), Making more types of services accessible and user-friendly to more types of user groups (ii), Making more types of feelings, opinions, voices and reactions visible from more types of user groups (iii) Specifically, in the above-presented cases traditional and classical theories, principles and models are re-introduced and can be integrated into state-of-the art data-driven approaches involving Machine Learning and neural networks, functioning as training data and seed data in Natural Language Processing applications where user requirements and customization are of particular interest and importance. A hybrid approach may be considered a compromise between speed and correctness / userfriendliness in (types of) NLP applications where the achievement of this balance plays a crucial role. In other words, a hybrid approach and the examples presented here target to prevent mechanisms from adopting human biases, ensuring fairness and socially responsible outcome and responsible Social Media. A hybrid approach and the examples presented here also target to customizing content to different linguistic and cultural groups, ensuring equitable information distribution. Here, we present characteristic examples with cases employing the re-introduction of four typical types of traditional concepts concerning classical theories, principles and models. These four typical classical theories, principles and models are also not considered to be flawless, however they can be transformed into practical strategies that can be integrated into evaluation modules, neural networks and training data (including knowledge graphs) and dialogue design. The proposed and discussed re-introduction of traditional concepts is not limited only to the particular models, principles and theories presented here. The first example concerns the application of a classic principle from Theoretical Linguistics. The concept employed in the second example concerns a model from the field of Linguistics and Translation. The third and the fourth examples demonstrate the interdisciplinary application of models and theoretical frameworks from the fields of Linguistics-Cognitive Science and Linguistics-Psychology respectively.

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Section: Integrating Implied and Unspoken Information In Knowledge Gr...mentioning

confidence: 99%

Section: Integrating Implied and Unspoken Information In Knowledge Gr...mentioning

confidence: 99%

Section: Integrating Implied and Unspoken Information In Knowledge Gr...mentioning

confidence: 99%

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GenAI and Socially Responsible AI in Natural Language Processing Applications: A Linguistic Perspective

Alexandris

2024

AAAI-SS

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Of Politics, Behavior and Commands: Processing Information Unspoken for Sentiment Analysis and Spoken Interaction Applications

Alexandris,

Trachanas,

Chatzipanayiotidis

2024

Lecture Notes in Computer Science

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Visualizing and Processing Information Not Uttered in Spoken Political and Journalistic Data: From Graphical Representations to Knowledge Graphs in an Interactive Application

Cited by 2 publications

References 25 publications

GenAI and Socially Responsible AI in Natural Language Processing Applications: A Linguistic Perspective

GenAI and Socially Responsible AI in Natural Language Processing Applications: A Linguistic Perspective

Of Politics, Behavior and Commands: Processing Information Unspoken for Sentiment Analysis and Spoken Interaction Applications

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