替换列表中的多个单词

Question

我已经使用

阅读了一个语料库

file_directory = 'path'
my_corpus = PlaintextCorpusReader(file_directory,'.*',encoding='latin1')

我执行预处理

    totalwords = my_corpus.words()
    docs = [my_corpus.words(f) for f in fids]
    docs2 = [[w.lower()for w in doc]for doc in docs]
    docs3 = [[w for w in doc if re.search('^[a-z]+$',w)]for doc in docs2]
    from nltk.corpus import stopwords
    stop_list = stopwords.words('english')
    docs4 = [[w for w in doc if w not in stop_list]for doc in docs3]
    wordscount = [w for doc in docs4 for w in doc]
    fd_dist_total = nltk.FreqDist(wordscount)
    print(fd_dist_total.most_common(common_words))

收到的输出是

words = [('ubs', 131), ('pacific', 130), ('us', 121), ('credit', 113), ('aum', 108), ('suisse', 102), ('asia', 98), ('arm', 95)]

我想知道是否可以将 'suisse' 的 102 个值替换为 'credit-suisse'。同样将 'asia' 替换为 'asia-pacific'

预期输出--

words = [('credit-suisse', 102), ('credit', 11) , ('pacific', 32), ('asia-pacific', 98)]

我尝试过使用

wordscount1 = [w.replace('asia','asia-pacific').replace('suisse', 'credit-suisse') for w in wordscount]

但是我遇到了明显的错误。

请指导我。

Answer 1

这是相当不明确的，因为我们不知道如何确保，例如，count('suisse') >= count('credit')。特别是，您希望：

• 用“credit-suisse”替换“suisse”，保持信用（第一个术语）credit minus suisse
• 但同时，您想将“asia”替换为“asia-pacific”，保持太平洋（第二术语）pacific minus asia（与第一种情况相反）

您必须明确说明该要求。也许您的替换术语是否以某种方式排序？无论如何，作为一个起点：

words = [('ubs', 131), ('pacific', 130), ('us', 121), 
         ('credit', 113), ('aum', 108), ('suisse', 102), 
         ('asia', 98), ('arm', 95)]

d = dict(words)

for terms in (('credit', 'suisse'), ('asia', 'pacific')):
    v1 = d.get(terms[1])
    if v1:
        d['-'.join(terms)] = v1
        v0 = d.get(terms[0],0)
        d[terms[0]] = v0-v1 # how to handle zero or negative values here ?
                            # it is unclear if it should be v1-v0 or v0-v1
                            # or even abs(v0-v1) 


from pprint import pprint

pprint(d)
pprint(d.items())

制作：

sh$ python3 p.py
{'arm': 95,
 'asia': -32,    # <- notice that value
 'asia-pacific': 130,
 'aum': 108,
 'credit': 11,   # <- and this one
 'credit-suisse': 102,
 'pacific': 130,
 'suisse': 102,
 'ubs': 131,
 'us': 121}
dict_items([('us', 121), ('suisse', 102), ('aum', 108), ('arm', 95),
            ('asia-pacific', 130), ('ubs', 131), ('asia', -32),
            ('credit', 11), ('credit-suisse', 102), ('pacific', 130)])