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README.md

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 # HomoRich: A Persian Homograph Dataset for G2P Conversion  
 
 HomoRich is the first large-scale, sentence-level Persian homograph dataset designed for grapheme-to-phoneme (G2P) conversion tasks. It addresses the scarcity of balanced, contextually annotated homograph data for low-resource languages. The dataset was created using a semi-automated pipeline combining human expertise and LLM-generated samples, as described in the paper:  
-**"Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models"**.  
+*[Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models](TODO)*.  
 
 ## Overview 
 The dataset contains 528,891 annotated Persian sentences (327,475 homograph-focused) covering 285 homograph words with 2-4 pronunciation variants each. Variants are equally represented (~500 samples each) to mitigate bias. The composition blends multiple sources for diversity, as shown below:
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 ## Usage  
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+### Dataset Creation and Processing
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+The `scripts` folder contains two key notebooks used in the dataset creation and processing pipeline:
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+1. `Generate\_Homograph\_Sentences.ipynb`: This notebook implements the prompt templates used to generate homograph-focused sentences as described in the paper, *[Fast, Not Fancy: Rethinking G2P with Rich Data and Rule-Based Models.](TODO)*
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+2. `Phonemize\_Sentences.ipynb`: This notebook applies the phonemization process based on the LLM-powered G2P method detailed in the *[LLM-Powered Grapheme-to-Phoneme Conversion: Benchmark and Case Study](TODO)* paper.
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+---
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 ## License  
 - **Dataset**: Released under **CC0-1.0** (public domain).  
 - **Code/Models**: **MIT License** (where applicable).