（1）下载spacy一直没成功，把pip install spacy改成conda install spacy就可以了；
（2）在命令行输入 python3 -m spacy download en 来下载英语语言包（如果是其他语言则下载其他包了），不过en现在最好用全称en_core_web_sm，这一步也可以先下载tar再pip install en_core_web_md-2.2.5.tar.gz（但是注意把文件放对路径）。
然后测试下代码：
import spacy import nltk # load spacy's English-language models en_nlp = spacy.load('en') # instantiate nltk's Porter stemmer stemmer = nltk.stem.PorterStemmer() # define function to compare lemmatization in spacy with stemming in nltk def compare_normalization(doc): # tokenize document in spacy doc_spacy = en_nlp(doc) # print lemmas found by spacy print("Lemmatization:") print([token.lemma_ for token in doc_spacy]) # print tokens found by Porter stemmer print("Stemming:") print([stemmer.stem(token.norm_.lower()) for token in doc_spacy])
发现又报错：
OSError: [E941] Can't find model 'en'. It looks like you're trying to load a model from a shortcut, which is obsolete as of spaCy v3.0. To load the model, use its full name instead: nlp = spacy.load("en_core_web_sm") For more details on the available models, see the models directory: https://spacy.io/models. If you want to create a blank model, use spacy.blank: nlp = spacy.blank("en")
是说上面load model的方法是spacy 3.0版本以前才这么用的，要改成nlp = spacy.load("en_core_web_sm")，然后就ok了，得到对应的spacy中的词形还原与nltk中的词干提取的对比结果：
Lemmatization: ['our', 'meeting', 'today', 'be', 'bad', 'than', 'yesterday', ',', 'I', 'be', 'scared', 'of', 'meet', 'the', 'client', 'tomorrow', '.'] Stemming: ['our', 'meet', 'today', 'wa', 'wors', 'than', 'yesterday', ',', 'i', 'am', 'scare', 'of', 'meet', 'the', 'client', 'tomorrow', '.']
