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2015 | OriginalPaper | Buchkapitel

EHLLDA: A Supervised Hierarchical Topic Model

verfasst von : Xian-Ling Mao, Yixuan Xiao, Qiang Zhou, Jun Wang, Heyan Huang

Erschienen in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Verlag: Springer International Publishing

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Abstract

In this paper, we consider the problem of modeling hierarchical labeled data – such as Web pages and their placement in hierarchical directories. The state-of-the-art model, hierarchical Labeled LDA (hLLDA), assumes that each child of a non-leaf label has equal importance, and that a document in the corpus cannot locate in a non-leaf node. However, in most cases, these assumptions do not meet the actual situation. Thus, in this paper, we introduce a supervised hierarchical topic models: Extended Hierarchical Labeled Latent Dirichlet Allocation (EHLLDA), which aim to relax the assumptions of hLLDA by incorporating prior information of labels into hLLDA. The experimental results show that the perplexity performance of EHLLDA is always better than that of LLDA and hLLDA on all four datasets; and our proposed model is also superior to hLLDA in terms of p@n.

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Vorheriges Kapitel Learning Distributed Representations of Uyghur Words and Morphemes

Nächstes Kapitel Graph-Based Dependency Parsing with Recursive Neural Network

http://dmoz.org/.

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Titel: EHLLDA: A Supervised Hierarchical Topic Model
verfasst von: Xian-Ling Mao
Yixuan Xiao
Qiang Zhou
Jun Wang
Heyan Huang
Verlag: Springer International Publishing
Buch: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data
Print ISBN: 978-3-319-25815-7

Electronic ISBN: 978-3-319-25816-4

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-25816-4_18

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