Add a new pipeline for the Relation Extraction task.

🚀 Feature request

Add a new pipeline option for the Relation Extraction task : nlp = pipeline('relation-extraction')

Motivation

Relation Extraction between named entities is a well-known NLP task. For example, when you get entities relative to medications (let’s say our entity types are DRUG and FORM (tablet, capsule, etc.)), you want to know which FORM entity goes with which DRUG entity, etc.

Reference: https://portal.dbmi.hms.harvard.edu/projects/n2c2-2018-t2/ This task is not limited to the biomedical domain.

Your contribution

I still need to play more with the HF API to contribute !

But, as I see it, the pipeline would return a list of dictionaries, each dictionary representing an identified relation in the text.

The relation extraction model would probably sit on top of the NER model.

There are implementations of such models here.

[NielsRogge]

For now, there’s only 1 model that is capable of performing relation extraction out-of-the-box, and that’s LUKE. You can use LukeForEntityPairClassification to classify the relationship between two entities in a sentence:

from transformers import LukeTokenizer, LukeForEntityPairClassification

tokenizer = LukeTokenizer.from_pretrained("studio-ousia/luke-large-finetuned-tacred")
model = LukeForEntityPairClassification.from_pretrained("studio-ousia/luke-large-finetuned-tacred")

text = "Beyoncé lives in Los Angeles."
entity_spans = [(0, 7), (17, 28)]  # character-based entity spans corresponding to "Beyoncé" and "Los Angeles"
inputs = tokenizer(text, entity_spans=entity_spans, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])

However, relation extraction is a task that is solved in many different ways. So it’s not straightforward to define a generic pipeline for it, in which you can plug different models.

[NielsRogge]

How should i go about incorporating this in my dataset?

I think you need to create several training examples for this single sentence. Each training example should be <sentence, entity 1, entity 2, relationship>. So indeed, option 1 is what I would do.

There are other approaches to relation extraction, in which one applies a binary classifier to each possible pair of entities (an example is this paper). However, LUKE doesn’t work that way.