Python-使用spaCy进行PoS标记和最小化
spaCy是最好的文本分析库之一。spaCy在大型信息提取任务方面表现出色，并且是世界上最快的之一。这也是准备用于深度学习的文本的最佳方法。spaCy比NLTKTagger和TextBlob更快，更准确。
如何安装？
pip install spacy python -m spacy download en_core_web_sm
示例
#importing loading the library
import spacy
# python -m spacy download en_core_web_sm
nlp = spacy.load("en_core_web_sm")
#POS-TAGGING
# Process whole documents
text = ("""My name is Vishesh. I love to work on data science problems. Please check out my github profile!""")
doc = nlp(text)
# Token and Tag
for token in doc:
print(token, token.pos_)
# You want list of Verb tokens
print("Verbs:", [token.text for token in doc if token.pos_ == "VERB"])
#Lemmatization : It is a process of grouping together the inflected #forms of a word so they can be analyzed as a single item, #identified by the word’s lemma, or dictionary form.
import spacy
# Load English tokenizer, tagger,
# parser, NER and word vectors
nlp = spacy.load("en_core_web_sm")
# Process whole documents
text = ("""My name is Vishesh. I love to work on data science problems. Please check out my github profile!""")
doc = nlp(text)
for token in doc:
print(token, token.lemma_)