Flair中的多语言通用词性标注（快速模型）

这是Flair中附带的快速多语言通用词性标注模型。

F1得分：92.88（覆盖英语、德语、法语、意大利语、荷兰语、波兰语、西班牙语、瑞典语、丹麦语、挪威语、芬兰语和捷克语的12个UD Treebanks）

预测通用词性标签：

基于 Flair embeddings 和LSTM-CRF。

示例：如何在Flair中使用

需要： Flair （pip install flair）

from flair.data import Sentence
from flair.models import SequenceTagger

# load tagger
tagger = SequenceTagger.load("flair/upos-multi-fast")

# make example sentence
sentence = Sentence("Ich liebe Berlin, as they say. ")

# predict NER tags
tagger.predict(sentence)

# print sentence
print(sentence)

# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('pos'):
    print(entity)

这将产生以下输出：

Span [1]: "Ich"   [− Labels: PRON (0.9999)]
Span [2]: "liebe"   [− Labels: VERB (0.9999)]
Span [3]: "Berlin"   [− Labels: PROPN (0.9997)]
Span [4]: ","   [− Labels: PUNCT (1.0)]
Span [5]: "as"   [− Labels: SCONJ (0.9991)]
Span [6]: "they"   [− Labels: PRON (0.9998)]
Span [7]: "say"   [− Labels: VERB (0.9998)]
Span [8]: "."   [− Labels: PUNCT (1.0)]

因此，在多语言句子“我爱柏林，就像他们说的那样”中，“我”和“they”被标记为代词（PRON），而“liebe”和“say”被标记为动词（VERB）。

训练：训练此模型的脚本

使用以下Flair脚本训练此模型：

from flair.data import MultiCorpus
from flair.datasets import UD_ENGLISH, UD_GERMAN, UD_FRENCH, UD_ITALIAN, UD_POLISH, UD_DUTCH, UD_CZECH, \
    UD_DANISH, UD_SPANISH, UD_SWEDISH, UD_NORWEGIAN, UD_FINNISH
from flair.embeddings import StackedEmbeddings, FlairEmbeddings

# 1. make a multi corpus consisting of 12 UD treebanks (in_memory=False here because this corpus becomes large)
corpus = MultiCorpus([
    UD_ENGLISH(in_memory=False),
    UD_GERMAN(in_memory=False),
    UD_DUTCH(in_memory=False),
    UD_FRENCH(in_memory=False),
    UD_ITALIAN(in_memory=False),
    UD_SPANISH(in_memory=False),
    UD_POLISH(in_memory=False),
    UD_CZECH(in_memory=False),
    UD_DANISH(in_memory=False),
    UD_SWEDISH(in_memory=False),
    UD_NORWEGIAN(in_memory=False),
    UD_FINNISH(in_memory=False),
])

# 2. what tag do we want to predict?
tag_type = 'upos'

# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)

# 4. initialize each embedding we use
embedding_types = [

    # contextual string embeddings, forward
    FlairEmbeddings('multi-forward-fast'),

    # contextual string embeddings, backward
    FlairEmbeddings('multi-backward-fast'),
]

# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)

# 5. initialize sequence tagger
from flair.models import SequenceTagger

tagger = SequenceTagger(hidden_size=256,
                        embeddings=embeddings,
                        tag_dictionary=tag_dictionary,
                        tag_type=tag_type,
                        use_crf=False)

# 6. initialize trainer
from flair.trainers import ModelTrainer

trainer = ModelTrainer(tagger, corpus)

# 7. run training
trainer.train('resources/taggers/upos-multi-fast',
              train_with_dev=True,
              max_epochs=150)

引用

使用此模型时，请引用以下论文。

@inproceedings{akbik2018coling,
  title={Contextual String Embeddings for Sequence Labeling},
  author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
  booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
  pages     = {1638--1649},
  year      = {2018}
}

问题？

Flair问题跟踪器可在此处 here 。