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- W2804963559 abstract "Topic labeling aims at providing a sound, possibly multi-words, label that depicts a topic drawn from a topic model. This is of the utmost practical interest in order to quickly grasp a topic informational content – the usual ranked list of words that maximizes a topic presents limitations for this task. In this paper, we introduce three new unsupervised n-gram topic labelers that achieve comparable results than the existing unsupervised topic labelers but following different assumptions. We demonstrate that combining topic labelers - even only two - makes it possible to target a 64% improvement with respect to single topic labeler approaches and therefore opens research in that direction. Finally, we introduce a fourth topic labeler that extracts representative sentences, using Dirichlet smoothing to add contextual information. This sentence-based labeler provides strong surrogate candidates when n-gram topic labelers fall short on providing relevant labels, leading up to 94% topic covering." @default.
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- W2804963559 date "2018-01-01" @default.
- W2804963559 modified "2023-10-16" @default.
- W2804963559 title "United We Stand: Using Multiple Strategies for Topic Labeling" @default.
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- W2804963559 doi "https://doi.org/10.1007/978-3-319-91947-8_37" @default.
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