python - 词形还原机制是否减少了语料库的大小？

Question

尊敬的社区成员，

在数据预处理过程中，将raw_data拆分为token后，我使用了流行的WordNet Lemmatizer来生成词干。我正在对具有 18953 个标记的数据集进行实验。

我的问题是，词形还原过程会减少语料库的大小吗？我很困惑，请在这方面提供帮助。感谢您的帮助!

Answer 1

词形还原将句子中的每个标记(aka form)转换为其词元形式(aka type):

>>> from nltk import word_tokenize
>>> from pywsd.utils import lemmatize_sentence

>>> text = ['This is a corpus with multiple sentences.', 'This was the second sentence running.', 'For some reasons, there is a need to second foo bar ran.']

>>> lemmatize_sentence(text[0]) # Lemmatized sentence example.
['this', 'be', 'a', 'corpus', 'with', 'multiple', 'sentence', '.']
>>> word_tokenize(text[0]) # Tokenized sentence example. 
['This', 'is', 'a', 'corpus', 'with', 'multiple', 'sentences', '.']
>>> word_tokenize(text[0].lower()) # Lowercased and tokenized sentence example.
['this', 'is', 'a', 'corpus', 'with', 'multiple', 'sentences', '.']

如果我们对句子进行词形还原，每个标记都应该接收相应的词形形式，所以没有。无论是 form 还是 type，“words”的数量都保持不变:

>>> num_tokens = sum([len(word_tokenize(sent.lower())) for sent in text])
>>> num_lemmas = sum([len(lemmatize_sentence(sent)) for sent in text])
>>> num_tokens, num_lemmas
(29, 29)


>>> [lemmatize_sentence(sent) for sent in text] # lemmatized sentences
[['this', 'be', 'a', 'corpus', 'with', 'multiple', 'sentence', '.'], ['this', 'be', 'the', 'second', 'sentence', 'running', '.'], ['for', 'some', 'reason', ',', 'there', 'be', 'a', 'need', 'to', 'second', 'foo', 'bar', 'ran', '.']]

>>> [word_tokenize(sent.lower()) for sent in text] # tokenized sentences
[['this', 'is', 'a', 'corpus', 'with', 'multiple', 'sentences', '.'], ['this', 'was', 'the', 'second', 'sentence', 'running', '.'], ['for', 'some', 'reasons', ',', 'there', 'is', 'a', 'need', 'to', 'second', 'foo', 'bar', 'ran', '.']]

“压缩”本身指的是在对句子进行词形还原后，整个语料库中代表的独特标记的数量，例如

>>> lemma_vocab = set(chain(*[lemmatize_sentence(sent) for sent in text]))
>>> token_vocab = set(chain(*[word_tokenize(sent.lower()) for sent in text]))
>>> len(lemma_vocab), len(token_vocab)
(21, 23)

>>> lemma_vocab
{'the', 'this', 'to', 'reason', 'for', 'second', 'a', 'running', 'some', 'sentence', 'be', 'foo', 'ran', 'with', '.', 'need', 'multiple', 'bar', 'corpus', 'there', ','}
>>> token_vocab
{'the', 'this', 'to', 'for', 'sentences', 'a', 'second', 'running', 'some', 'is', 'sentence', 'foo', 'reasons', 'with', 'ran', '.', 'need', 'multiple', 'bar', 'corpus', 'there', 'was', ','}

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注意:词形还原是一个预处理步骤。但它应该不用词形还原形式覆盖您的原始语料库。