Alternatives to NER taggers for long, heterogeneous phrases?
It looks like your ingredient list is easy to split into a list. In that case you don't really need a sequence tagger; I wouldn't treat this problem as phrase extraction or NER. What I would do is train a classifier on different items in the list to label them as "food" or "non-food". You should be able to start with rules and train a basic classifier using anything really.
Before training a model, an even simpler step would be to run each list item through a PoS tagger (say spaCy), and if there's a verb you can guess that it's not a food item.
I am looking for ideas/thoughts on the following problem:
I am working with food ingredient data such as: milk, sugar, eggs, flour, may contain nuts
From such piece of text I want to be able to identify and extract phrases like may contain nuts, to preprocess them separately
These kinds of phrases can change quite a lot in terms of length and content. I thought of using NER taggers, but I don't know if they will do the job correctly as they are mainly used for identifying single-word entities...
Any ideas on what to use as a phrase-entity-recognition system? Also which package would you use? Cheers