August 2017
Beginner to intermediate
340 pages
8h 42m
English
Content preview from Mastering Machine Learning with Spark 2.x
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Stemming and lemmatization
One extremely popular step in NLP is to stem words back to their root form. For example, "accounts" and "accounting" would both be stemmed to "account," which at first blush seems very reasonable. However, stemming falls prey to the following two areas, which you should be aware of:
1. Over-stemming: This is when stemming fails to keep two words with distinct meanings separate. For example, stem ("general," "genetic") = "gene".
2. Under-stemming: This is the inability to reduce words with the same meaning to their root forms. For example, stem ("jumping," "jumpiness") = jumpi but stem ("jumped," "jumps") = "jump." In this example, we know that each of the preceding terms are simply an inflection of the root word ...
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