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by Vishal Chahal, Balaji Kadambi | Published October 20, 2017
AnalyticsData ScienceObject StoragePlatform as a ServicePythonCloud
Text analytics involves getting insights from text content in documents, books, social media, and various other sources. A common requirement is to find the correlation of text content across sources to get a comprehensive picture. This code pattern uses Watson Natural Language Understanding (NLU), Python Natural Language Processing Toolkit (NLTK), and IBM Watson Studio to build a graph of entities with attributes and use its relationship with other entities to correlate text content across various sources.
In this code pattern, you will use Jupyter notebooks in IBM DSX to correlate text content across documents with the Python NLTK toolkit and IBM Watson NLU. The correlation algorithm is driven by an input configuration JSON that contains the rules and grammar for building the relations. You can modify the JSON configuration document to obtain better correlation results between text content across documents.
After completing this pattern, you’ll learn how to:
Find the detailed steps for this pattern in the README. The steps will show you how to:
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