Document visualization: an overview of current research

Gan, Qihong; Zhu, Min; Li, Mingzhao; Liang, Ting; Cao, Yu; Zhou, Baoyao

doi:10.1002/wics.1285

Cited by 39 publications

(32 citation statements)

References 50 publications

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“…Alencar et al [2] describe roughly 30 techniques by means of data source, underlying text representation, visual metaphor, layout, and supported user tasks. Gan et al [15] discuss approx. 40 techniques with regard to data source, user tasks, visual representation, and supported interactions.…”

Section: Related Surveysmentioning

confidence: 99%

Text visualization techniques: Taxonomy, visual survey, and community insights

Kucher

Kerren

2015

2015 IEEE Pacific Visualization Symposium (PacificVis)

155

129

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Text visualization has become a growing and increasingly important subfield of information visualization. Thus, it is getting harder for researchers to look for related work with specific tasks or visual metaphors in mind. In this paper, we present an interactive visual survey of text visualization techniques that can be used for the purposes of search for related work, introduction to the subfield and gaining insight into research trends. We describe the taxonomy used for categorization of text visualization techniques and compare it to approaches employed in several other surveys. Finally, we present results of analyses performed on the entries data.

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Section: Related Surveysmentioning

confidence: 99%

Text visualization techniques: Taxonomy, visual survey, and community insights

Kucher

Kerren

2015

2015 IEEE Pacific Visualization Symposium (PacificVis)

155

129

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“…A word cloud [26] is a useful tool to analyze and find out the most common words in a document which are summarized in a simplified figure. Gan et al [26] considered the word clouds as the most versatile methods over many document visualization tools. The generated figures used the size and the color to indicate clusters of words with almost the same frequencies.…”

Section: Word Cloudsmentioning

confidence: 99%

A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History

et al. 2019

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Detecting and determining which systems or subsystems of a wind turbine have more failures is essential to improve their design, which will reduce the costs of generating wind power. Two of the most critical failures, the generator and gearbox, are analyzed and characterized with four metrics. This failure analysis usually begins with the identification of the turbine’s condition, a process normally performed by an expert examining the wind turbine’s service history. This is a time-consuming task, as a human expert has to examine each service entry. To automate this process, a new methodology is presented here, which is based on a set of steps to preprocess and decompose the service history to find relevant words and sentences that discriminate an unhealthy wind turbine period from a healthy one. This is achieved by means of two classifiers fed with the matrix of terms from the decomposed document of the training wind turbines. The classifiers can extract essential words and determine the conditions of new turbines of unknown status using the text from the service history, emulating what a human expert manually does when labelling the training set. Experimental results are promising, with accuracy and F-score above 90% in some cases. Condition monitoring system can be improved and automated using this system, which helps the expert in the tedious task of identifying the relevant words from the turbine service history. In addition, the system can be retrained when new knowledge becomes available and may therefore always be as accurate as a human expert. With this new tool, the expert can focus on identifying which systems or subsystems can be redesigned to increase the efficiency of wind turbines.

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“…Visualization as a means of illustration has a long tradition in linguistics, e.g., through spectograms for sound waves, tables for paradigms or graphs and attribute-value matrices for syntactic information. Besides such traditionally established visualizations, recent years have seen the emergence of new visualization ideas coming out of the field of VA (Keim et al, 2008) for the analysis and representation of linguistic data (Sun et al, 2013;Liu et al, 2014;Gan et al, 2014). A considerable amount of research has specifically focused on the visualization of historical linguistic change.…”

Section: Relevant Workmentioning

confidence: 99%

ParHistVis: Visualization of Parallel Multilingual Historical Data

Kalouli¹,

Kehlbeck²,

Sevastjanova³

et al. 2019

Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change

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The study of language change through parallel corpora can be advantageous for the analysis of complex interactions between time, text domain and language. Often, those advantages cannot be fully exploited due to the sparse but high-dimensional nature of such historical data. To tackle this challenge, we introduce ParHistVis: a novel, free, easy-to-use, interactive visualization tool for parallel, multilingual, diachronic and synchronic linguistic data. We illustrate the suitability of the components of the tool based on a use case of word order change in Romance wh-interrogatives.

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Document visualization: an overview of current research

Cited by 39 publications

References 50 publications

Text visualization techniques: Taxonomy, visual survey, and community insights

Text visualization techniques: Taxonomy, visual survey, and community insights

A Text-Mining Approach to Assess the Failure Condition of Wind Turbines Using Maintenance Service History

ParHistVis: Visualization of Parallel Multilingual Historical Data

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