Visualization Techniques for Topic Model Checking
Authors: Jaimie Murdock, Colin Allen
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present the Topic Explorer, which advances the state-of-the-art in topic model visualization for document-document and topic-document relations. It brings topic models to life in a way that fosters deep understanding of both corpus and models, allowing users to generate interpretive hypotheses and to suggest further experiments. Such tools are an essential step toward assessing whether topic modeling is a suitable technique for AI and cognitive modeling applications. Figure 1: Screenshot of the Topic Explorer showing a 20-topic model (left) and a 40-topic model (right) centered on the Stanford Encyclopedia of Philosophy (SEP) article on Turing Machines. |
| Researcher Affiliation | Academia | Jaimie Murdock and Colin Allen Program in Cognitive Science Indiana University {jammurdo,colallen}@indiana.edu |
| Pseudocode | No | The paper describes the functionality of the Topic Explorer but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All software described in this document is published under an open-source license at Git Hub. The Topic Explorer is available at http://github.com/inpho/topic-explorer. The VSM framework is available at http://github.com/inpho/vsm. |
| Open Datasets | No | The paper mentions 'Live demos trained on a digital encyclopedia, newspaper stories, and a selection of digitized books' and references the 'Stanford Encyclopedia of Philosophy (SEP) article on Turing Machines' in Figure 1. However, it does not provide concrete access information (specific links, DOIs, repositories, or formal citations) for these datasets. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or methodology for training, validation, and testing splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions 'Latent Dirichlet Allocation (LDA Blei, Jordan, and Ng (2003))' and the 'In Ph O VSM framework' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper discusses different numbers of topics (K) for LDA models but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rates, batch sizes) or training configurations. |