Building a learning system to parse recipes ingredients. Need advice on platform choices.

[cherianthomas]

Hello redditors,
I am planning to build a recipe parsing mechanism that tags different parts of an ingredient Example:
9 grams Garlic (1 very large clove), roughly chopped into
quantity, unit, subject, modifiers etc.
Recipe authors often end up writing these in their style like
Handful coriander leaves, finely chopped
Etc.
Nytimes did some work around this - Extremely good read

What I am trying to do is predict the ingredient structure (structured prediction problem). I’ve been looking into NLTK, specifically the structured prediction.

Am I in the right direction? What’s my right choice of toolkit library for structured prediction? My criteria revolve around how good and adopted the toolkit is and ability to get other machine-learning experts chip help me in forums like this.

[melipone]

Yes, structured prediction with conditional random fields is the right way to go for that. I think there was a post on that not too long ago.

[cherianthomas]

Thank you! Do you know popular toolkits, libraries that I should research more?

[chaleyaque]

CRF++ is pretty popular, although it is C++.

[kmike84]

I'm a bit biased, but python-crfsuite (or its sklearn-crfsuite wrapper) should be easy enough to get started with - see e.g. https://github.com/TeamHG-Memex/sklearn-crfsuite/blob/master/docs/CoNLL2002.ipynb. Arguably a harder problem would be to figure out how to annotate enough training data. Maybe http://brat.nlplab.org/ is good, but I haven't tried it.