Extracting food items from sentences - algorithm

Question

Given a sentence:
I had peanut butter and jelly sandwich and a cup of coffee for
breakfast
I want to be able to extract the following food items from it:
peanut butter and jelly sandwich
coffee
Till now, using POS tagging, I have been able to extract the individual food items, i.e.
peanut, butter, jelly, sandwich, coffee
But like I said, what I need is peanut butter and jelly sandwich instead of the individual items.
Is there some way of doing this without having a corpus or database of food items in the backend?

Answer 1

You can attempt it without using a trained set which contains a corpus of food items, but the approach shall work without it too.
Instead of doing simple POS tagging, do a dependency parsing combined with POS tagging.
That way would be able to find relations between multiple tokens of the phrase, and parsing the dependency tree with restricted conditions like noun-noun dependencies you shall be able to find relevant chunk.
You can use spacy for dep parsing. Here is output from displacy :
https://demos.explosion.ai/displacy/?text=peanut%20butter%20and%20jelly%20sandwich%20is%20delicious&model=en&cpu=1&cph=1
You can use freely available data here, or something better:
https://en.wikipedia.org/wiki/Lists_of_foods as a training set to
create a base set of food items (the hyperlinks in the crawled tree)
Based on the dependency parsing on your new data, you can keep
enriching the base data. For example: if 'butter' exists in your
corpus, and 'peanut butter' is a frequently encountered pair of
tokens, then 'peanut' and 'peanut butter' also get added to the
corpus.
The corpus can be maintained in a file which can be loaded in memory
while processing, or database like redis,aerospike etc.
Make sure you work with normalized i.e. small cased, special
characters cleaned, words lemmatized/stemmed, both in corpus and the
processing data. That would increase your coverage and accuracy.

Answer 2

First extract all Noun phrases using NLTK's Chunking (code copied from here):
import nltk
import re
import pprint
from nltk import Tree
import pdb
patterns="""
NP: {<JJ>*<NN*>+}
{<JJ>*<NN*><CC>*<NN*>+}
{<NP><CC><NP>}
{<RB><JJ>*<NN*>+}
"""
NPChunker = nltk.RegexpParser(patterns)
def prepare_text(input):
sentences = nltk.sent_tokenize(input)
sentences = [nltk.word_tokenize(sent) for sent in sentences]
sentences = [nltk.pos_tag(sent) for sent in sentences]
sentences = [NPChunker.parse(sent) for sent in sentences]
return sentences
def parsed_text_to_NP(sentences):
nps = []
for sent in sentences:
tree = NPChunker.parse(sent)
print(tree)
for subtree in tree.subtrees():
if subtree.label() == 'NP':
t = subtree
t = ' '.join(word for word, tag in t.leaves())
nps.append(t)
return nps
def sent_parse(input):
sentences = prepare_text(input)
nps = parsed_text_to_NP(sentences)
return nps
if __name__ == '__main__':
print(sent_parse('I ate peanut butter and beef burger and a cup of coffee for breakfast.'))
This will POS tag your sentences and uses a regex parser to extract Noun Phrases.
1.Define and Refine your noun phrase regex
You'll need to change the patterns regex to define and refine your Noun phrases.
For example is telling the parser than an NP followed by a coordinator (CC) like ''and'' and another NP is itself an NP.
2.Change from NLTK POS tagger to Stanford POS tagger
Also I noted that NLTK's POS tagger is not performing very well (e.g. It considers had peanut as a verb phrase. You can change the POS tagger to Stanford Parser if you want.
3.Remove smaller noun phrases:
After you have extracted all the Noun phrases for a sentence, you can remove the ones that are part of a bigger noun phrase. For example in the following example beef burger and peanut butter should be removed because
they're a part of a bigger noun phrase peanut butter and beef burger.
4.Remove noun phrases which none of the words are in a food lexicon
you will get noun phrases like school bus. if none of school and bus is in a food lexicon that you can compile from Wikipedia or WordNet then you remove the noun phrase. In this case remove cup and breakfast because they're not hopefully in your food lexicon.
The current code returns
['peanut butter and beef burger', 'peanut butter', 'beef burger', 'cup', 'coffee', 'breakfast']
for input
print(sent_parse('I ate peanut butter and beef burger and a cup of coffee for breakfast.'))

Answer 3

Too much for a comment, but not really an answer:
I think you would at least get closer if when you got two foods without a proper separator and combined them into one food. That would give peanut butter, jelly sandwich, coffee.
If you have correct English you could detect this case by count/non-count. Correcting the original to "I had a peanut butter and jelly sandwich and a cup of coffee for breakfast". Butter is non-count, you can't have "a butter", but you can have "a sandwich". Thus the a must apply to sandwich and despite the and "peanut butter" and "jelly sandwich" must be the same item--"peanut butter and jelly sandwich". Your mistaken sentence would parse the other way, though!
I would be very surprised if you could come up with general rules that cover every case, though. I would come at this sort of thing figuring that a few would leak and need a database to catch.

Answer 4

You could search for n-grams in your text where you vary the value of n. For example, if n=5 then you would extract "peanut butter and jelly sandwich" and "cup of coffee for breakfast", depending on where you start your search in the text for groups of five words. You won't need a corpus of text or a database to make the algorithm work.

Answer 5

A rule based approach with a lexicon of all food items would work here.
You can use GATE for the same and use JAPE rules with it.
In the above example your jape rule would have a condition to find all (np cc np) && np in "FOOD LEIXCON"
Can share a detailed jape code in an event you plan to go this route.