Reading Tea Leaves: How Humans Interpret Topic Models
Jonathan Chang low, Sean Gerrish low, Chong Wang, Jordan Boyd‐Graber, David M. Blei
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What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Latent dirichlet allocation | 2003 | 27,078 |
| Indexing by latent semantic analysis | 1990 | 12,850 |
| A solution to Plato's problem: The latent semantic analysis theory of acquisition, induct… | 1997 | 6,147 |
| NLTK | 2002 | 3,318 |
| Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy | 1997 | 2,230 |
| Probabilistic Latent Semantic Analysis | 2013 | 2,093 |
| NLTK: The Natural Language Toolkit | 2002 | 1,943 |
| Cheap and fast---but is it good? | 2008 | 1,928 |
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Software Framework for Topic Modelling with Large Corpora | 2010 | 3,809 |
| Towards A Rigorous Science of Interpretable Machine Learning | 2017 | 3,190 |
| Exploring the Space of Topic Coherence Measures | 2015 | 2,240 |
Links
Topics
| Topic Modeling | Computer Science |
| Advanced Text Analysis Techniques | Computer Science |
| Natural Language Processing Techniques | Computer Science |
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