从Keras的imdb数据集中恢复原始文本
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从Keras的imdb数据集中恢复原始文本

我想从Keras的imdb数据集中恢复imdb的原始文本。

首先,当我加载Keras的imdb数据集时,它返回单词索引序列。

>>> (X_train, y_train), (X_test, y_test) = imdb.load_data()
>>> X_train[0]
[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 22665, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 21631, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 19193, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 10311, 8, 4, 107, 117, 5952, 15, 256, 4, 31050, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 12118, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32]

我找到了imdb.get_word_index method(),它返回单词索引字典,例如{'create':984,'make':94,…}。为了进行转换,我创建了索引词字典。

>>> word_index = imdb.get_word_index()
>>> index_word = {v:k for k,v in word_index.items()}

然后,我尝试还原原始文本,如下所示。

>>> ' '.join(index_word.get(w) for w in X_train[5])
"the effort still been that usually makes for of finished sucking ended cbc's an because before if just though something know novel female i i slowly lot of above freshened with connect in of script their that out end his deceptively i i"

我的英语不好,但是我知道这句话有些奇怪。

为什么会这样呢?如何还原原始文字?

参考资料:
Stack Overflow
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共 2 个回答
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您可以使用keras.utils.data_utils中的get_file获得不删除停用词的原始数据集:

path = get_file('imdb_full.pkl',
               origin='https://s3.amazonaws.com/text-datasets/imdb_full.pkl',
                md5_hash='d091312047c43cf9e4e38fef92437263')
f = open(path, 'rb')
(training_data, training_labels), (test_data, test_labels) = pickle.load(f)

学分-杰里米·霍华德fast.ai课程第5课

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您的示例是胡言乱语的,它比仅缺少一些停用词要糟糕得多。

如果您重新阅读[ keras.datasets.imdb.load_data ]( https://keras.io/datasets/#imdb-movie-reviews-sentiment-classification )方法的start_charoov_charindex_from参数的文档他们解释发生了什么:

start_char :整数。序列的开始将用此字符标记。设置为1是因为0通常是填充字符。

oov_char :整数。由于num_words或skip_top限制而被切掉的单词将被替换为该字符。

index_from :int。使用此索引和更高的索引来索引实际单词。

您倒置的字典假定单词索引从1开始。

但是我返回的喀拉拉邦的索引具有<START><UNKNOWN>作为索引12 。 (并且假定您将对<PADDING>使用0 )。

这对我有用:

import keras
NUM_WORDS=1000 # only use top 1000 words
INDEX_FROM=3   # word index offset

train,test = keras.datasets.imdb.load_data(num_words=NUM_WORDS, index_from=INDEX_FROM)
train_x,train_y = train
test_x,test_y = test

word_to_id = keras.datasets.imdb.get_word_index()
word_to_id = {k:(v+INDEX_FROM) for k,v in word_to_id.items()}
word_to_id["<PAD>"] = 0
word_to_id["<START>"] = 1
word_to_id["<UNK>"] = 2
word_to_id["<UNUSED>"] = 3

id_to_word = {value:key for key,value in word_to_id.items()}
print(' '.join(id_to_word[id] for id in train_x[0] ))

标点符号丢失了,仅此而已:

"<START> this film was just brilliant casting <UNK> <UNK> story
 direction <UNK> really <UNK> the part they played and you could just
 imagine being there robert <UNK> is an amazing actor ..."
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