sentences=sentences.replace(',','') sentences=sentences.replace('.','')# 将句子里面的.去掉 sentences=sentences.split()# 将句子分开为单个的单词,分开后产生的是一个列表sentences # print(sentences) count_dict={} forsentenceinsentences:
string = "Apple, Banana, Orange, Blueberry" print(string.split()) Output: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 ['Apple,', 'Banana,', 'Orange,', 'Blueberry'] 我们可以看到字符串没有很好地拆分,因为拆分的字符串包含 ,。我们可以使用 sep=',' 在有, 的地方进行拆分: 代码语...
lines.append(line)print(" %i lines read from'%s' with size: %5.2f kb"%(len(lines),t,sys.getsizeof(lines)/1024.))# Constructa big stringofclean text text=" ".join(lineforlineinlines)# splitonsentences(period+space)delim=". "sentences=[_+delimfor_intext.split(delim)]#regexes are th...
tokenized_corpus_path -> string -- 经过分词的语料集地址 num_topics -> integer -- 主题数目 num_words -> integer -- 主题词数目 max_lines -> integer -- 每次读入的最大行数 split -> string -- 文档的词之间的分隔符 max_df -> integer -- 避免常用词,过滤超过该阈值的词 """ # 存放所有...
sentences = sent_tokenize(text) sentences_cleaned = [] for sent in sentences: words = nlkt_word_tokenize(sent) words = [w for w in words if w not in string.punctuation] words = [w for w in words if not w.lower() in stop_words] words = [w.lower() for w in words] sentences...
nlp=stanfordnlp.Pipeline()doc=nlp("Barack Obama was born in Hawaii.")forsentenceindoc.sentences:print(sentence.dependencies_string())# 打印依存关系 1. 2. 3. 4. 5. 6. 7. 解释: 这段代码展示了如何使用Stanford CoreNLP进行依存句法分析,输出句子内部词语之间的依存关系。
for sentence in doc.sentences: print(sentence.dependencies_string()) # 打印依存关系 解释: 这段代码展示了如何使用Stanford CoreNLP进行依存句法分析,输出句子内部词语之间的依存关系。 6. PyTorch Text 如果你对深度学习感兴趣,那么PyTorch Text绝对值得一试。它是基于PyTorch构建的,专为文本数据设计,可以方便地...
Notice that this example is really a single sentence, reporting the speech of Mr. Lucian Gregory. However, the quoted speech contains several sentences, and these have been split into individual strings. This is reasonable behavior for most applications. ...
假设您已经在系统中安装了"en_core_web_sm"模型，如果没有，您可以通过在终端中运行以下命令轻松安装：...
print(res['quiz']['sport']) # Dump data as string data = json.dumps(res) print(data) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 5读取 CSV 数据 import csv with open('test.csv','r') as csv_file: reader =csv.reader(csv_file) next(reader) # Skip first row for row ...