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Tagging and linking entities

As of Flair 0.12 we ship an experimental entity linker trained on the Zelda dataset. The linker does not only tag entities, but also attempts to link each entity to the corresponding Wikipedia URL if one exists.

Example 1: Entity linking on a single sentence​​

To illustrate, let's use the example sentence "Kirk and Spock met on the Enterprise.":

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

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

# make a sentence
sentence = Sentence('Kirk and Spock met on the Enterprise.')

# predict entity links
tagger.predict(sentence)

# iterate over predicted entities and print
for label in sentence.get_labels():
    print(label)

This should print:

Span[0:1]: "Kirk" → James_T._Kirk (0.9969)
Span[2:3]: "Spock" → Spock (0.9971)
Span[6:7]: "Enterprise" → USS_Enterprise_(NCC-1701-D) (0.975)

As we can see, the linker can resolve that:

  • "Kirk" refers to the entity "James_T._Kirk"

  • "Spock" refers to "Spock" (ok, that one was easy)

  • "Enterprise" refers to the "USSEnterprise(NCC-1701-D)"

    Not bad, eh? However, that last prediction is not quite correct as Star Trek fans will know. Entity linking is a hard task and we are working to improve the accuracy of our model.

Example 2: Entity linking on a text document (multiple sentences)​

Entity linking typically works best when applied to a whole document instead of only a single sentence.

To illustrate how this works, let's use the following short text: "Bayern played against Barcelona. The match took place in Barcelona."

In this case, split the text into sentences and pass a list of Sentence objects to the .predict() method:

from flair.nn import Classifier
from flair.splitter import SegtokSentenceSplitter

# example text with many sentences
text = "Bayern played against Barcelona. The match took place in Barcelona."

# initialize sentence splitter
splitter = SegtokSentenceSplitter()

# use splitter to split text into list of sentences
sentences = splitter.split(text)

# predict tags for sentences
tagger = Classifier.load('linker')
tagger.predict(sentences)

# iterate through sentences and print predicted labels
for sentence in sentences:
    print(sentence)

This should print: 

Sentence[5]: "Bayern played against Barcelona." → ["Bayern"/FC_Bayern_Munich, "Barcelona"/FC_Barcelona]
Sentence[7]: "The match took place in Barcelona." → ["Barcelona"/Barcelona]

As we can see, the linker can resolve what the two mentions of "Barcelona" refer to:

  • "Bayern" refers to the soccer club "FC Bayern Munich"
  • the first mention of "Barcelona" refers to the soccer club "FC Barcelona"
  • the second mention of "Barcelona" refers to the city of "Barcelona"