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) |
|
Chunking |
English |
Conll-2000 |
96.22 (F1) |
(fast model) |
|
‘frame’ |
Frame Detection |
English |
Propbank 3.0 |
97.54 (F1) |
|
Frame Detection |
English |
Propbank 3.0 |
97.31 (F1) |
(fast model) |
|
‘negation-speculation’ |
Negation / speculation |
English |
Bioscope |
80.2 (F1) |
|
‘communicative-functions’ |
detecting function of sentence in research paper (BETA) |
English |
scholarly papers |
||
‘de-historic-indirect’ |
historical indirect speech |
German |
@redewiedergabe project |
87.94 (F1) |
|
‘de-historic-direct’ |
historical direct speech |
German |
@redewiedergabe project |
87.94 (F1) |
|
‘de-historic-reported’ |
historical reported speech |
German |
@redewiedergabe project |
87.94 (F1) |
|
‘de-historic-free-indirect’ |
historical free-indirect speech |
German |
@redewiedergabe project |
87.94 (F1) |