April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Content preview from 精通特征工程
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50
｜
第
3
章
('as', 'RB'),
('awful', 'JJ'),
('as', 'IN'),
('trying', 'VBG'),
('to', 'TO'),
('find', 'VB'),
('a', 'DT'),
('date', 'NN')]
>>> print([np for np in blob_df[4].noun_phrases])
['got', 'goldberg', 'arizona', 'new position', 'june', 'new doctor', 'nyc']
可见，通过不同程序库找出的名词短语并不完全一样。
spaCy
找出的短语中包括英文中的
一些普通词，如“
a
”和“
the
”， 而
TextBlob
则去掉了这些词。由此可知，不同程序库中
认定名词短语的规则引擎是有区别的。你还可以编写自己的词性关系来定义要搜寻的文本
块。你可以参考（
Bird
等，
2009
）来从头开始研究使用
Python
进行文本分块的方法。
3.4
小结
词袋表示法简单易懂，容易计算，并对分类和搜索任务非常有效。但有时单个单词还是太
简单了，无法表述出文本中的某些信息。为了解决这个问题，我们要求助于更长的序列。
n
元词袋是词袋的一种自然推广，它的概念非常好理解，计算起来也和词袋一样容易。
n
元词袋可以生成大量互不相同的
n
元词，它增加了特征存储成本，在模型训练和预测阶
段也需要更多计算能力。对于同样数量的数据点，
n
元词袋使得特征空间的维度大大增加。
因此，数据变得特别稀疏。
n
越大，存储和计算的成本就越高 ...
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