Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Jointly Modeling Topics and Intents with Global Order Structure
Authors: Bei Chen, Jun Zhu, Nan Yang, Tian Tian, Ming Zhou, Bo Zhang
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments in both unsupervised and supervised settings, results show the superiority of our model over several state-of-the-art baselines. |
| Researcher Affiliation | Collaboration | Dept. of Comp. Sci. & Tech., State Key Lab of Intell. Tech. & Sys., Center for Bio-Inspired Computing Research, TNList, Tsinghua University, Beijing, 100084, China Microsoft Research Asia, Beijing, 100080, China |
| Pseudocode | Yes | As shown in Fig.3, we present an approximate three-step algorithm to obtain the canonical permutation π0. Step 1: We compute π d for each labeled document sd... Step 2: We introduce variables gij(i, j [K])... Step 3: We obtain the π0 by calculating the topological sequence of G. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use two real datasets: 1) Chemical (Guo et al. 2010): It contains 965 abstracts of scientific papers...; and 2) Elements (Chen et al. 2009): It consists of 118 articles from the English Wikipedia... |
| Dataset Splits | No | For supervised classification, the paper states 'we randomly choose 20% documents; annotate their sentences with intent labels; and use them for training. Our goal is to learn the intent labels for the sentences in the remaining 80% documents.' However, it does not explicitly mention a separate validation set or split. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'SVMLight tools (Joachims 1998)' but does not provide specific version numbers for it or any other software dependencies. |
| Experiment Setup | Yes | For hyperparameters, we set θ0 = 0.1, λ0 = 0.1, α0 = 0.1, β0 = 0.1 and γ0 = 1... ν0 is set to be 0.1 times the number of documents in the corpus. For EGMM-LDA, we set the regularization parameter c to be 0.1. We set ρ0 = 2 for all the experiments except for Elements with K = 10, in which we set ρ0 = 1. |