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- W4297775547 abstract "Academic researchers often need to face with a large collection of research papers in the literature. This problem may be even worse for postgraduate students who are new to a field and may not know where to start. To address this problem, we have developed an online catalog of research papers where the papers have been automatically categorized by a topic model. The catalog contains 7719 papers from the proceedings of two artificial intelligence conferences from 2000 to 2015. Rather than the commonly used Latent Dirichlet Allocation, we use a recently proposed method called hierarchical latent tree analysis for topic modeling. The resulting topic model contains a hierarchy of topics so that users can browse the topics from the top level to the bottom level. The topic model contains a manageable number of general topics at the top level and allows thousands of fine-grained topics at the bottom level. It also can detect topics that have emerged recently." @default.
- W4297775547 created "2022-10-01" @default.
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- W4297775547 date "2016-09-28" @default.
- W4297775547 modified "2023-09-30" @default.
- W4297775547 title "Topic Browsing for Research Papers with Hierarchical Latent Tree Analysis" @default.
- W4297775547 doi "https://doi.org/10.48550/arxiv.1609.09188" @default.
- W4297775547 hasPublicationYear "2016" @default.
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