因此,无论何时我尝试加载先前保存的SpaCy NER模型,我都会得到一个核心转储。 if os.path.isfile(model_path):
ner.model.load(model_path)
for itn in range(5):
random.shuffle(TRAIN_DATA)
for raw_text, entity_offsets in TRAI
我有以下代码打开目录中的文件,对它们运行spaCy NLP,输出依赖项将信息解析到新目录中的文件中。 import spacy, os
nlp = spacy.load('en')
path1 = 'C:/Path/to/my/input'
path2 = '../output'
for file in os.listdir(path1):
with open(file, e
我试图从下面的段落结构提取这种类型的信息: women_ran men_ran kids_ran walked
1 2 1 3
2 4 3 1
3 6 5 2
text = ["On Tuesday, one women ran on the street while 2 men ran and 1 child ran on the sid
from spacy.en import English
from numpy import dot
from numpy.linalg import norm
parser = English()
# you can access known words from the parser's vocabulary
nasa = parser.vocab['NASA']
# cos
我正在努力研究一个句子中的主题提取问题,以便我可以根据主题获得情感。我在python2.7中使用nltk来达到这个目的。以下面的句子为例: Donald Trump is the worst president of USA, but Hillary is better than him 他我们可以看到,Donald Trump和Hillary是两个科目,有关Donald Trump情绪是负的,但