Serendip: Topic model-driven visual exploration of text corpora

Alexander, Eric; Kohlmann, Joe; Valenza, Robin; Witmore, Michael; Gleicher, Michael

doi:10.1109/vast.2014.7042493

Cited by 95 publications

(65 citation statements)

References 27 publications

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“…Most often they rely on topic modeling realized with latent Dirichlet allocation [6], this modeling is used to establish a similarity between the documents, this similarity is then used to link the documents together in different ways, such as a graph, and then allow a graphical exploration of this graph [7], [8], [9]. Some approaches focus on the human aspects of the document exploration interface [10], others tend to detect the evolution of scientific topics in the time [11], or try to promote serendipity [12].…”

Section: Related Workmentioning

confidence: 99%

Semantic Search-by-Examples for Scientific Topic Corpus Expansion in Digital Libraries

Al-Natsheh¹,

Martinet²,

Muhlenbach³

et al. 2017

2017 IEEE International Conference on Data Mining Workshops (ICDMW)

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Abstract-In this article we address the problem of expanding the set of papers that researchers encounter when conducting bibliographic research on their scientific work. Using classical search engines or recommender systems in digital libraries, some interesting and relevant articles could be missed if they do not contain the same search key-phrases that the researcher is aware of. We propose a novel model that is based on a supervised active learning over a semantic features transformation of all articles of a given digital library. Our model, named Semantic Search-byExamples (SSbE), shows better evaluation results over a similar purpose existing method, More-Like-This query, based on the feedback annotation of two domain experts in our experimented use-case. We also introduce a new semantic relatedness evaluation measure to avoid the need of human feedback annotation after the active learning process. The results also show higher diversity and overlapping with related scientific topics which we think can better foster transdisciplinary research.

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Section: Related Workmentioning

confidence: 99%

Semantic Search-by-Examples for Scientific Topic Corpus Expansion in Digital Libraries

Al-Natsheh¹,

Martinet²,

Muhlenbach³

et al. 2017

2017 IEEE International Conference on Data Mining Workshops (ICDMW)

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“…However, even if scholars noted their potential, for example by creating serendipity, and different metrics have been proposed for evaluating the number and correctness of these clusters, this is still an extremely challenging task, typically due to the difficulties of interpreting the clusters output by the algorithms. 12 …”

Section: Supervised and Unsupervised Text Analysesmentioning

confidence: 99%

Semi-supervised Textual Analysis and Historical Research Helping Each Other: Some Thoughts and Observations

Nanni¹,

Kümper²,

Ponzetto³

2016

IJHAC

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“…Then the topic name, year and value of the topic are shown with the pointer. Comparing to [16], which uses a matrix-like style to present topics, ours has more explicit and simple visibility and operations.…”

Section: Cited Relationshipmentioning

confidence: 99%

Data Mining, Management and Visualization in Large Scientific Corpuses

Wei

Zhao

et al. 2016

E-Learning and Games

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Abstract. Organizing scientific papers helps efficiently derive meaningful insights of the published scientific resources, enables researchers grasp rapid technological change and hence assists new scientific discovery. In this paper, we experiment text mining and data management of scientific publications for collecting and presenting useful information to support research. For efficient data management and fast information retrieval, four data storages are employed: a semantic repository, an index and search repository, a document repository and a graph repository, taking full advantage of their features and strength. The results show that the combination of these four repositories can effectively store and index the publication data with reliability and efficiency and hence supply meaningful information to support scientific research.

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Serendip: Topic model-driven visual exploration of text corpora

Cited by 95 publications

References 27 publications

Semantic Search-by-Examples for Scientific Topic Corpus Expansion in Digital Libraries

Semantic Search-by-Examples for Scientific Topic Corpus Expansion in Digital Libraries

Semi-supervised Textual Analysis and Historical Research Helping Each Other: Some Thoughts and Observations

Data Mining, Management and Visualization in Large Scientific Corpuses

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