基于多项研究，我发现以下重要的比较分析:
如果我们看文本，词汇化很可能会返回更正确的输出，对吗？不仅正确，而且缩短了版本，我在这条线上做了一个实验:
sentence ="having playing in today gaming ended with greating victorious"
但是当我运行了lemmatizer和stemmization的代码时，我得到了以下结果:
['have', 'play', 'in', 'today', 'game', 'end', 'with', 'great', 'victori'] ['having', 'playing', 'in', 'today', 'gaming', 'ended', 'with', 'greating', 'victorious']
第一个是词干，一切看起来都很好，除了Victori(应该是胜利的权利)和第二个是Lemmalized(它们都是正确的，但在原始的形式)，那么在这种情况下，哪个选项是好的？短版本和大部分不正确的还是长版本和正确的？
import nltk
from nltk.tokenize import word_tokenize,sent_tokenize
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
from nltk.stem import PorterStemmer,WordNetLemmatizer
mylematizer =WordNetLemmatizer()
mystemmer =PorterStemmer()
nltk.download('stopwords')
sentence ="having playing in today gaming ended with greating victorious"
words =word_tokenize(sentence)
# print(words)
stemmed =[mystemmer.stem(w) for w in words]
lematized=[mylematizer.lemmatize(w) for w in words ]
print(stemmed)
print(lematized)
# mycounter =CountVectorizer()
# mysentence ="i love ibsu. because ibsu is great university"
# # print(word_tokenize(mysentence))
# # print(sent_tokenize(mysentence))
# individual_words=word_tokenize(mysentence)
# stops =list(stopwords.words('english'))
# words =[w for w in individual_words if w not in stops and w.isalnum() ]
# reduced =[mystemmer.stem(w) for w in words]
# new_sentence =' '.join(words)
# frequencies =mycounter.fit_transform([new_sentence])
# print(frequencies.toarray())
# print(mycounter.vocabulary_)
# print(mycounter.get_feature_names_out())
# print(new_sentence)
# print(words)
# # print(list(stopwords.words('english')))