Tagging other things

This tutorial gives you a tour of other crazy models shipped with Flair. These include:

• tagging semantic frames

• chunking text

• relation extraction

• others

Let’s get started!

Semantic Frame Detection

For English, we provide a pre-trained model that detects semantic frames in text, trained using Propbank 3.0 frames. This provides a sort of word sense disambiguation for frame evoking words, and we are curious what researchers might do with this.

Here’s an example:

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

# load model
tagger = Classifier.load('frame')

# make English sentence
sentence = Sentence('George returned to Berlin to return his hat.')

# predict NER tags
tagger.predict(sentence)

# go through tokens and print predicted frame (if one is predicted)
for token in sentence:
    print(token)

This should print:

Token[0]: "George"
Token[1]: "returned" → return.01 (0.9951)
Token[2]: "to"
Token[3]: "Berlin"
Token[4]: "to"
Token[5]: "return" → return.02 (0.6361)
Token[6]: "his"
Token[7]: "hat"
Token[8]: "."

As we can see, the frame detector makes a distinction in the sentence between two different meanings of the word ‘return’. ‘return.01’ means returning to a location, while ‘return.02’ means giving something back.

Syntactic Chunking

For English, we provide a model for chunking verb and noun phrases, trained using CoNLL 2000.

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

# load model
tagger = Classifier.load('chunk')

# make English sentence
sentence = Sentence('The quick brown fox jumps over the lazy dog.')

# predict NER tags
tagger.predict(sentence)

# print the chunks
for chunk in sentence.get_labels():
  print(chunk)

This should print:

Span[0:4]: "The quick brown fox" → NP (0.9914)
Span[4:5]: "jumps" → VP (1.0)
Span[5:6]: "over" → PP (0.9967)
Span[6:9]: "the lazy dog" → NP (0.9991)

This tells us for instance that “the quick brown fox” and “the lazy dog” form syntactic units in this sentence.

Tagging Relations

Relations hold between two entities. For instance, a text like “George was born in Washington” names two entities and also expresses that there is a born_in relationship between both.

We added an experimental relation extraction model trained over a modified version of TACRED. You must use this model together with an entity tagger. Here is an example:

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

# 1. make example sentence
sentence = Sentence("George was born in Washington")

# 2. load entity tagger and predict entities
tagger = Classifier.load('ner-fast')
tagger.predict(sentence)

# check which named entities have been found in the sentence
entities = sentence.get_labels('ner')
for entity in entities:
    print(entity)

# 3. 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)
print("")

# Use the `get_labels()` method with parameter 'relation' to iterate over all relation predictions. 
for label in sentence.get_labels('relation'):
    print(label)

This should print:

Span[0:1]: "George" → PER (0.9971)
Span[4:5]: "Washington" → LOC (0.9847)

Relation[0:1][4:5]: "George -> Washington" → born_in (1.0)

Indicating that a born_in relationship holds between “George” and “Washington”!

List of Other Models

We end this section with a list of all other models we currently ship with Flair:

ID	Task	Language	Training Dataset	Accuracy	Contributor / Notes
‘chunk’	Chunking	English	Conll-2000	96.47 (F1)
‘chunk-fast’	Chunking	English	Conll-2000	96.22 (F1)	(fast model)
‘frame’	Frame Detection	English	Propbank 3.0	97.54 (F1)
‘frame-fast’	Frame Detection	English	Propbank 3.0	97.31 (F1)	(fast model)
‘negation-speculation’	Negation / speculation	English	Bioscope	80.2 (F1)
‘de-historic-indirect’	historical indirect speech	German	@redewiedergabe project	87.94 (F1)	redewiedergabe
‘de-historic-direct’	historical direct speech	German	@redewiedergabe project	87.94 (F1)	redewiedergabe
‘de-historic-reported’	historical reported speech	German	@redewiedergabe project	87.94 (F1)	redewiedergabe
‘de-historic-free-indirect’	historical free-indirect speech	German	@redewiedergabe project	87.94 (F1)	redewiedergabe

Next

Congrats, you learned about some other models we have in Flair!

So far, we only focused on predicting for single sentences. Next, let’s discuss how to create predictions for a whole corpus of documents.