In this notebook, we will walk-through a unique example demonstrating how you can apply optimization to assess text dissimilarity. There are numerous potential applications such as in detecting plagiarism, information retrieval, clustering, text categorization, topic detection, question answer session, machine translation and text summarization.
The Word Mover’s Distance (WMD) is a popular measure of text similarity, which measures the semantic distance between two documents. In this notebook, we will achieve two goals:
This modeling tutorial is at the introductory level, where we assume that you know Python and that you have a background on a discipline that uses quantitative methods.
You may find it helpful to refer to the documentation of the Gurobi Python API.
Click on the button below to access the example in Google Colab, which is a free, online Jupyter Notebook environment that allows you to write and execute Python code through your browser.
Check out the Colab Getting Started Guide for full details on how to use Colab Notebooks as well as create your own.
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