[Question]: Additional granularity in relation extraction

[hyshandler]

Question

I have a corpus that contains brief one paragraph bios from thousands of people. I'm hoping to extract more granular relations from it. For instance, with the text "Tim Cook is the president and CEO of Apple", the relation output is "Tim Cook -> Apple" → employee_of and "Apple -> Tim Cook" → works_for (0.9953). I want this to extract "Tim Cook -> Apple" → president and CEO. I understand I can hand label this sentence (and others) and train an additional layer, but I'm wondering if there's another (hopefully less manual) way to extract the relation. Thanks!

from flair.nn import Classifier
from flair.data import Sentence

text = "Tim Cook is the president and CEO of Apple"

# load the model
tagger = Classifier.load('ner')

# make a sentence
sentence = Sentence(text)

# predict NER tags
tagger.predict(sentence)

# load relation extractor
extractor = Classifier.load('relations')

# predict relations
extractor.predict(sentence)

# check which relations have been found
relations = sentence.get_labels('relation')
for relation in relations:
    print(relation)

Relation[0:2][6:7]: "Tim Cook -> Apple" → employee_of (1.0)
Relation[6:7][0:2]: "Apple -> Tim Cook" → works_for (0.9953)

[alanakbik]

Hello @hyshandler, I think an OpenIE model could do what you are looking for. We don't currently have one in Flair, but we are building one that will likely be added in a future release.

[stale]

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