I have some experience with sentiment analysis in natural language processing, but want to learn some new algorithms and techniques for a project I am working on. In particular, I am interested in a measure of how much a text is ``obfuscated'', such as the one provided as a black-box by http://www.blablameter.com/

What would be a good place to start learning about such algorithms?



These sort of algorithms often predate machine learning and have their roots in the linguistic community. A commonly used such measure of "clarity" is the Gunning Fog Index, which estimates the years of formal education a person needs to understand the text on the first reading.

An alternative is the Flesch–Kincaid readability test that quantifies how difficult a passage in English is to understand.

I have seen both examples above (and others) being used in NLP problems. The short answer to your question is "search in the linguistics domain rather than the machine learning community to start learning about such algorithms".

| cite | improve this answer | |

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.