adjustments from btuya

Author: Evgeny-Egorov-Projects

topicnet/demos/README.md

@@ -1,26 +1,29 @@
 # Demo
 This section provides demonstrations of how to use this library in NLP tasks.
 
-1. [RTL-Wiki-Preprocessing](RTL-Wiki-Preprocessing.ipynb) --  notebook describing how to get a Wikipedia dataset and write data in VW format.
+1. [RTL-Wiki-Preprocessing](RTL-Wiki-Preprocessing.ipynb) --  notebook working with a dataset introduced in [1]. It serves as an example of a typical preprocessing pipeline: getting a dataset, lemmatizing it, extracting n-grams/collocations, writing data in VW format
 
 2. [RTL-Wiki-Building-Topic-Mode](RTL-Wiki-Building-Topic-Model.ipynb) --  notebook with first steps to build topic model by consequently tuning its hyperparameters
 
 3. [Visualizing-Your-Model-Documents](Visualizing-Your-Model-Documents.ipynb) -- notebook providing a fresh outlook on unstructured document collection with the help of a topic model
 
 4. [20NG-Preprocessing](20NG-Preprocessing.ipynb) -- preparing data from a well-know 20 Newsgroups dataset
 
-5. [20NG-GenSim-vs-TopicNet](20NG-GenSim-vs-TopicNet.ipynb) -- a comparison between two topic models build by Gensim and TopicNet library. In the notebook we compare model topics by calculating their [UMass coherence measure](https://palmetto.demos.dice-research.org/) and using Jaccard measure to compare topic top-tokens diversity
+5. [20NG-GenSim-vs-TopicNet](20NG-GenSim-vs-TopicNet.ipynb) -- a comparison between two topic models build by Gensim and TopicNet library. In the notebook we compare model topics by calculating their UMass coherence measure with the help of [Palmetto](https://palmetto.demos.dice-research.org/) and using Jaccard measure to compare topic top-tokens diversity
 
 6. [PostNauka-Building-Topic-Model](PostNauka-Building-Topic-Model.ipynb)-- an analog of the RTL-Wiki notebook performed on the corpus of Russian pop-science articles given by postnauka.ru
 
-7. [PostNauka-Recipe](PostNauka-Recipe) -- a demonstration of rapid-prototyping methods provided by the library
+7. [PostNauka-Recipe](PostNauka-Recipe.ipynb) -- a demonstration of rapid-prototyping methods provided by the library
 
 8. [Coherence-Maximization-Recipe](Coherence-Maximization-Recipe.ipynb) -- a recipe for hyperparameter search in regard to custom Coherence metric
 
 9. [Topic-Prior-Regularizer-Tutorial](Topic-Prior-Regularizer-Tutorial.ipynb) -- a demonstration of the approach to learning topics from the unbalanced corpus
 
 10. [Making-Decorrelation-and-Topic-Selection-Friends](Making-Decorrelation-and-Topic-Selection-Friends.ipynb) -- reproduction of a very complicated experiment on automatically learning optimal number of topics from the collection. Hurdle is -- both needed regularizers when working together nullify token-topic matrix.
 
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+[1](https://dl.acm.org/doi/10.5555/2984093.2984126) Jonathan Chang, Jordan Boyd-Graber, Sean Gerrish, Chong Wang, and David M. Blei. 2009. Reading tea leaves: how humans interpret topic models. In Proceedings of the 22nd International Conference on Neural Information Processing Systems (NIPS’09). Curran Associates Inc., Red Hook, NY, USA, 288–296.
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 P.S. All the guides are supposed to contain **working** examples of the library code.
 If you happen to find code that is no longer works, please write about it in the library issues.