def seg_sentence(sentence): sentence_seged=jieba.cut(sentence.strip()) stopwords=stopwordslist('data\CEstopWords.txt') outstr='' for word in sentence_seged: if word not in stopwords: if word !='\t': outstr += word outstr += " " return outstr
时间: 2024-02-14 07:35:08 浏览: 34
这是一个Python函数,它的作用是对中文句子进行分词并去除停用词(指在文本处理中无实际意义的词语,如“的”、“是”等)。其中使用了jieba库进行分词,调用了一个名为stopwordslist的函数读取停用词表,最后将分好的词重新组合成字符串并返回。如果你有需要对中文文本进行处理的任务,这个函数可以作为一个基础工具来使用。
相关问题
写出该段代码的伪代码:def seg_depart(sentence): # 对文档中的每一行进行中文分词 #print("正在分词") sentence_depart = jieba.cut(sentence.strip()) # 引进停用词列表 stopwords = stopwordslist() # 输出结果为outstr outstr = '' # 去停用词 for word in sentence_depart: if word not in stopwords: if word != '\t': outstr += word outstr += " " return outstr
伪代码如下:
开始定义函数 seg_depart(sentence):
将 sentence 传入当前函数中
对 sentence 进行切割并存储到 word_list 列表中
创建一个空的 sentence_depart 字符串
遍历 word_list 列表中的每一个词:
判断当前词是否为空格,若是则跳过本次循环
对当前词进行字符串拼接,并加上空格
返回拼接好的 sentence_depart 字符串
结束函数定义
请在注释处填入代码完成对训练集和测试集的结巴分词from paddlenlp.datasets import load_dataset def read(data_path): data_set = [] with open(data_path, 'r', encoding='utf-8') as f: for line in f: l = line.strip('\n').split('\t') if len(l) != 2: print (len(l), line) words, labels = line.strip('\n').split('\t') data_set.append((words,labels)) return data_set train_ds = read(data_path='train.txt') dev_ds = read(data_path='dev.txt') test_ds = read(data_path='test.txt') for i in range(5): print("sentence %d" % (i), train_ds[i][0]) print("sentence %d" % (i), train_ds[i][1]) print(len(train_ds),len(dev_ds)) import jieba def data_preprocess(corpus): data_set = [] ####填结巴分词代码 for text in corpus: seg_list = jieba.cut(text) data_set.append(" ".join(seg_list)) return data_set train_corpus = data_preprocess(train_ds) test_corpus = data_preprocess(test_ds) print(train_corpus[:2]) print(test_corpus[:2])
from paddlenlp.datasets import load_dataset
def read(data_path):
data_set = []
with open(data_path, 'r', encoding='utf-8') as f:
for line in f:
l = line.strip('\n').split('\t')
if len(l) != 2:
print (len(l), line)
words, labels = line.strip('\n').split('\t')
data_set.append((words,labels))
return data_set
train_ds = read(data_path='train.txt')
dev_ds = read(data_path='dev.txt')
test_ds = read(data_path='test.txt')
for i in range(5):
print("sentence %d" % (i), train_ds[i][0])
print("sentence %d" % (i), train_ds[i][1])
print(len(train_ds),len(dev_ds))
import jieba
def data_preprocess(corpus):
data_set = []
for text in corpus:
seg_list = jieba.cut(text[0])
data_set.append((" ".join(seg_list), text[1]))
return data_set
train_corpus = data_preprocess(train_ds)
test_corpus = data_preprocess(test_ds)
print(train_corpus[:2])
print(test_corpus[:2])
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