print(long_words(3, "The quick brown fox jumps over the lazy dog")) -> This line calls the 'long_words' function with the input parameters 3 and "The quick brown fox jumps over the lazy dog". The function return
>>> list_of_words = ['one', 'two', 'list', '', 'dict'] >>> sorted(list_of_words) ['', 'dict', 'list', 'one', 'two'] >>> 元组 参数除了传入列表,还可以是元组,结果依然是返回一个新列表。 >>> tuple_of_words = ('one', 'two', 'list', '', 'dict') >>> sorted(tu...
The sents() function divides the text up into its sentences, where each sentence is a list of words(把文本分割成句子,每个句子是一个由单词组成的列表): >>> macbeth_sentences = gutenberg.sents('shakespeare-macbeth.txt')>>> macbeth_sentences[['[', 'The', 'Tragedie', 'of', 'Macbeth', ...
words = ['This', 'is', 'a', 'list', 'of', 'words'] result = max(words, key=len) print(result) # 'words' 17、列表推导式 代码语言:javascript 代码运行次数:0 运行 AI代码解释 li = [num for num in range(0, 10)] print(li) # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 18...
pigLatin = [] # A list of the words in Pig Latin. for word in message.split(): # Separate the non-letters at the start of this word: prefixNonLetters = '' while len(word) > 0 and not word[0].isalpha(): prefixNonLetters += word[0] ...
If we want to split a string to list based on whitespaces, then we don’t need to provide any separator to the split() function. Also, any leading and trailing whitespaces are trimmed before the string is split into a list of words. So the output will remain same for string s = '...
Figure 2-5. Lexicon terminology: Lexical entries for two lemmas having the same spelling (homonyms), providing part-of-speech and gloss information.两个词目的词汇主体有相同的拼写,提供词性和注释信息。 The simplest kind of lexicon is nothing more than a sorted list of words. Sophisticated lexicon...
Write a Python program that prints long text, converts it to a list, and prints all the words and the frequency of each word. Sample Solution: Python Code : # Define a multiline string containing a passage about the United States Declaration of Independence.string_words='''United States De...
stop_words = set() for i in con: i = i.replace("\n", "") # 去掉读取每一行数据的\n stop_words.add(i) for word in seg_list_exact: # 设置停用词并去除单个词 if word not in stop_words and len(word) > 1: result_list.append(word) ...
''' text: a list of words, all text from the training dataset word_to_idx: the dictionary from word to idx idx_to_word: idx to word mapping word_freq: the frequency of each word word_counts: the word counts ''' super(WordEmbeddingDataset, self).__init__() ...