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Best Paper Award at the iV 2021

Our paper on “Visual Analytics and Similarity Search – Interest-based Similarity Search in Scientific Data” at the iV2021 conference was honored with “The Best Paper Award” for the innovative contribution in terms of originality of concepts and application. The “Best Paper Awards” is given to contributions which will be selected by the committee among the papers presented in iV2021 and applied for the award. Study’s relevance to the symposium’s scope, its scientific contribution, writing/presentation style will be considered in the evaluation process as well.

The Information Visualisation Conference (iV) is an international conference that aims to provide a foundation for integrating the human-centered, technological and strategic aspects of information visualization to promote international exchange, cooperation and development.

The post Best Paper Award at the iV 2021 appeared first on Human-Computer Interaction & Visual Analyitics Reasearch Group (vis) at Darmstadt University of Applied Sciences (h_da).

Paper accepted at 24rd Internation Conference Information Visualization (iV 2020)

We are very glad to be accepted for presenting our paper titled “Comparison of Full-text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing” at the high-class conference Information Visualisation Conference (iV 2020). Due to Corona epidemic the conference is hold virtually. The iV 2020 is an international conference that aims to provide a foundation for integrating the human-centered, technological and strategic aspects of information visualization to promote international exchange, cooperation and development.

Comparison of Full-text Articles and Abstracts for Visual Trend Analytics through Natural Language Processing

Abstract:
Scientific publications are an essential resource for detecting emerging trends and innovations in a very early stage, by far earlier than patents may allow. Thereby Visual Analytics systems enable a deep analysis by applying commonly unsupervised machine learning methods and investigating a mass amount of data. A main question from the Visual Analytics viewpoint in this context is, do abstracts of scientific publications provide a similar analysis capability compared to their corresponding full-texts? This would allow to extract a mass amount of text documents in a much faster manner. We compare in this paper the topic extraction methods LSI and LDA by using full text articles and their corresponding abstracts to obtain which method and which data are better suited for a Visual Analytics system for Technology and Corporate Foresight. Based on a easy replicable natural language processing approach, we further investigate the impact of lemmatization for LDA and LSI. The comparison will be performed qualitative and quantitative to gather both, the human perception in visual systems and coherence values. Based on an application scenario a visual trend analytics system will further illustrate the outcomes.

Link to paper/fulltext: DOI: 10.1109/10.1109/IV51561.2020.00065

More information about the technology: Scitics for Visual Trend Analytics