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A Novel Document Generation Process for Topic Detection Based on Hierarchical Latent Tree Models

机器翻译基于分层潜树模型为主题检测一种新文档生成处理

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【摘要】We propose a novel document generation process based on hierarchical latent tree models (HLTMs) learned from data. An HLTM has a layer of observed word variables at the bottom and multiple layers of latent variables on top. For each document, the generative process rst samples values for the latent variables layer by layer via logic sampling, then draws relative frequencies for the words conditioned on the values of the latent variables, and nally generates words for the document using the relative word frequencies. The motivation for this work is to take word counts into consideration with HLTMs. In comparison with LDA-based hierarchical document generation processes, the new process achieves drastically better model t with much fewer parameters. It also yields more meaningful topics and topic hierarchies. It is the new state- of-the-art for the hierarchical topic detection.

【作者】Chen, Peixian; Chen, Zhourong; Zhang, Nevin Lianwen;

【作者单位】

【年(卷),期】2019,,

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【总页数】13

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【网站名称】香港科技大学图书馆

【栏目名称】所有文件

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