Machine Learning for Text Extraction

In a previous post we looked at the use of Natural Language Processing techniques in text extraction. Several steps are involved in the processing as each document passes through a pipeline of chained tasks.

A deep pipeline can take several seconds for a document. So if one is dealing with thousands of documents an hour the processing requirements could make the system nonviable. Care needs to be taken to evaluate the trade-off between the improvements in accuracy caused by adding pipeline tasks with the additional processing power that it entails.
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Text Extraction using Natural Language Processing

A few months ago I was asked to look into an email processing problem. We needed to extract event related information from consumer-originated email. As a traditional programmer the first instinct was to think in terms of regular expressions and lookup tables! Experience quickly tempered that thought and I decided to look at Natural Language Processing.
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