Training a NER System Using a Large Dataset
In a previous article, we studied training a NER (Named-Entity-Recognition) system from the ground up, using the Groningen Meaning Bank Corpus. This article is a continuation of that tutorial. The main purpose of this extension to training a NER is to:
- Replace the classifier with a Scikit-Learn Classifier
- Train a NER on a larger subset of the training data
- Increase accuracy
- Understand Out Of Core Learning
What was wrong with the initial system you might ask. There wasn’t anything fundamentally wrong with the process. In fact, it’s a great didactical example, and we can build upon it. This is where it was lacking:
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