• 文本预处理通常包括四个步骤：
  • 读入文本
  • 分词(Tokenization)
  • 建立词典(vocab)，将每个词映射到唯一的索引(index)
  • 根据词典，将文本序列转为索引序列，方便输入模型
  • 建立词向量矩阵

读入文本

class ZOLDatesetReader:
    @staticmethod
    def __data_Counter__(fnames):
        # 计数器
        jieba_counter = Counter()
        label_counter = Counter()
        max_length_text = 0
        min_length_text = 1000
        max_length_img = 0
        min_length_img = 1000
        lengths_text = []
        lengths_img = []
        for fname in fnames:
            with open(fname, 'r', encoding='utf-8', newline='\n', errors='ignore') as fin:
                lines = fin.readlines()
                for i in range(0, len(lines), 4):
                    text_raw = lines[i].strip()
                    imgs = lines[i + 1].strip()[1:-1].split(',')
                    aspect = lines[i + 2].strip()
                    polarity = lines[i + 3].strip()

                    length_text = len(text_raw)
                    length_img = len(imgs)

                    if length_text >= max_length_text:
                        max_length_text = length_text
                    if (length_text <= min_length_text):
                        min_length_text = length_text
                    lengths_text.append(length_text)

                    if length_img >= max_length_img:
                        max_length_img = length_img
                    if (length_img <= min_length_img):
                        min_length_img = length_img
                    lengths_img.append(length_img)


                    jieba_counter.update(text_raw)
                    label_counter.update([polarity])
        print(label_counter)

去停用词

from nltk.corpus import stopwords
nltk.download('stopwords')
stopwords_list = stopwords.words('english')
text = " What? You don't love python?"
text  = text .split()
    for word in text :
      if word in stopwords_list:
        text .remove(word)

自定义停词表

def jieba_cut(text):
    text = dp_txt(text)
    stopwords = {}.fromkeys([line.rstrip() for line in open('./datasets/stopwords.txt', encoding='utf-8')])
    segs = jieba.cut(text, cut_all=False)

    final = ''
    for seg in segs:
        seg = str(seg)
        if seg not in stopwords:
            final += seg
    seg_list = jieba.cut(final, cut_all=False)
    text_cut = ' '.join(seg_list)
    return text_cut

建立词典

self.word2idx = {}
self.idx2word = {}
self.idx = 1

def fit_on_text(self, text):
    if self.lower:
        text = text.lower()
    words = text.split()
    for word in words:
          if word not in self.word2idx:
              self.word2idx[word] = self.idx
              self.idx2word[self.idx] = word
              self.idx += 1

文本序列映射

def text_to_sequence(self, text, isaspect=False , reverse=False):
        if self.lower:
            text = text.lower()
        words = text.split()
        unknownidx = len(self.word2idx)+1
        sequence = [self.word2idx[w] if w in self.word2idx else unknownidx for w in words]
        if len(sequence) == 0:
            sequence = [0]
        pad_and_trunc = 'post'  # use post padding together with torch.nn.utils.rnn.pack_padded_sequence
        if reverse:
            sequence = sequence[::-1]
        if isaspect:
            return Tokenizer.pad_sequence(sequence, self.max_aspect_len, dtype='int64',
                                          padding=pad_and_trunc, truncating=pad_and_trunc)
        else:
            return Tokenizer.pad_sequence(sequence, self.max_seq_len, dtype='int64',
                                          padding=pad_and_trunc, truncating=pad_and_trunc)

建立词向量矩阵

def build_embedding_matrix(word2idx, embed_dim, type):
    embedding_matrix_file_name = '{0}_{1}_embedding_matrix.dat'.format(str(embed_dim), type)
    if os.path.exists(embedding_matrix_file_name):
        print('loading embedding_matrix:', embedding_matrix_file_name)
        embedding_matrix = pickle.load(open(embedding_matrix_file_name, 'rb'))
    else:
        print('loading word vectors...')
        embedding_matrix = np.random.rand(len(word2idx) + 2, embed_dim)  # idx 0 and len(word2idx)+1 are all-zeros
        fname = '../../datasets/GloveData/glove.6B.' + str(embed_dim) + 'd.txt' \
            if embed_dim != 300 else '../../datasets/ChineseWordVectors/sgns.target.word-character.char1-2.dynwin5.thr10.neg5.dim' + str(embed_dim) + '.iter5'
        word_vec = load_word_vec(fname, word2idx=word2idx)
        print('building embedding_matrix:', embedding_matrix_file_name)
        for word, i in word2idx.items():
            vec = word_vec.get(word)
            if vec is not None:
                # words not found in embedding index will be all-zeros.
                embedding_matrix[i] = vec
        pickle.dump(embedding_matrix, open(embedding_matrix_file_name, 'wb'))
    return embedding_matrix

标签：word,matrix,text,self,length,embedding,文本,预处理
来源： https://www.cnblogs.com/ArdenWang/p/16146751.html