Semantic Word Clouds with Background Corpus Normalization and t-distributed Stochastic Neighbor Embedding Article Swipe

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Erich Schubert , Andreas Spitz , M. Weiler , Johanna Geiß , Michael Gertz ·

YOU? · · 2017 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1708.03569

Many word clouds provide no semantics to the word placement, but use a random layout optimized solely for aesthetic purposes. We propose a novel approach to model word significance and word affinity within a document, and in comparison to a large background corpus. We demonstrate its usefulness for generating more meaningful word clouds as a visual summary of a given document. We then select keywords based on their significance and construct the word cloud based on the derived affinity. Based on a modified t-distributed stochastic neighbor embedding (t-SNE), we generate a semantic word placement. For words that cooccur significantly, we include edges, and cluster the words according to their cooccurrence. For this we designed a scalable and memory-efficient sketch-based approach usable on commodity hardware to aggregate the required corpus statistics needed for normalization, and for identifying keywords as well as significant cooccurences. We empirically validate our approch using a large Wikipedia corpus.

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Computer science Natural language processing Normalization (sociology) Artificial intelligence Distributional semantics Word (group theory) Word embedding Tag cloud Scalability Treebank Semantics (computer science) Latent Dirichlet allocation Embedding Lexicon Sketch Topic model Semantic similarity Algorithm Parsing Visualization Linguistics Database Anthropology Sociology Philosophy Programming language

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1708.03569
PDF: https://arxiv.org/pdf/1708.03569
OA Status: green
Cited By: 16
References: 36
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.1708.03569

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Title: Semantic Word Clouds with Background Corpus Normalization and t-distributed Stochastic Neighbor Embedding

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Language: en

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Publication year: 2017

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Publication date: 2017-08-11

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Authors: Erich Schubert, Andreas Spitz, M. Weiler, Johanna Geiß, Michael Gertz

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Landing page: https://arxiv.org/abs/1708.03569

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Concepts: Computer science, Natural language processing, Normalization (sociology), Artificial intelligence, Distributional semantics, Word (group theory), Word embedding, Tag cloud, Scalability, Treebank, Semantics (computer science), Latent Dirichlet allocation, Embedding, Lexicon, Sketch, Topic model, Semantic similarity, Algorithm, Parsing, Visualization, Linguistics, Database, Anthropology, Sociology, Philosophy, Programming language

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Cited by: 16

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Citations by year (recent): 2024: 2, 2023: 2, 2022: 2, 2021: 3, 2020: 1

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