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

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Peixian Chen, Zhourong Chen, Nevin L. Zhang

Computation and Language · Submitted 12 Dec 2017

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim It is the new state-of-the-art for the hierarchical topic detection.

how it is stated · asserted 1.00 read the record →

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arXiv ID1712.04116 (version 3)
Pith IDCPUYQ7XG, Pith's permanent record of this paper and its review
CitationNevin L. Zhang, Peixian Chen, Zhourong Chen. "A Novel Document Generation Process for Topic Detection based on Hierarchical Latent Tree Models." arXiv:1712.04116 (2017). https://pith.science/paper/CPUYQ7XG
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,433 input tokens.

What the abstract itself states, as probabilities: claims first 0.19 · states limitation 0.06 · human participants 0.02 · controlled comparison 0.63 · randomized assignment 0.01 · states quantitative result 0.07 · compares with prior results 0.96 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; .

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, we first sample values for the latent variables layer by layer via logic sampling, then draw relative frequencies for the words conditioned on the values of the latent variables, and finally generate words for the document using the relative word frequencies. The motivation for the 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 fit 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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We propose a novel document generation process based on hierarchical latent tree models (HLTMs) learned from data. contribution 1.00 0.92 0.11 asserted 0.99
s1 An HLTM has a layer of observed word variables at the bottom and multiple layers of latent variables on top. method 0.71 0.13 0.02 asserted 1.00
s2 For each document, we first sample values for the latent variables layer by layer via logic sampling, then draw relative frequencies for the words conditioned on the values of the latent variables, and finally generate words for the document using the relative word frequencies. method 1.00 0.79 0.04 asserted 0.98
s3 The motivation for the work is to take word counts into consideration with HLTMs. gap 0.68 0.27 0.03 asserted 0.98
s4 In comparison with LDA-based hierarchical document generation processes, the new process achieves drastically better model fit with much fewer parameters. result 1.00 0.87 0.35 asserted 1.00
s5 It also yields more meaningful topics and topic hierarchies. result 0.96 0.86 0.17 asserted 1.00
s6 It is the new state-of-the-art for the hierarchical topic detection. result 0.83 0.67 0.53 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.45.

What was examined, and what was not

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  • full text
  • figures and tables
  • references and citing sentences
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  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

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, we first sample values for the latent variables layer by layer via logic sampling, then draw relative frequencies for the words conditioned on the values of the latent variables, and finally generate words for the document using the relative word frequencies. The motivation for the 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 fit 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.

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