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Dictionary stemmers work quite differently from algorithmic stemmers. Instead of applying a standard set of rules to each word, they simply look up the word in the dictionary. Theoretically, they could produce much better results than an algorithmic stemmer. A dictionary stemmer should be able to do the following:
Return the correct root word for irregular forms such as
Recognize the distinction between words that are similar but have
different word senses—for example,
In practice, a good algorithmic stemmer usually outperforms a dictionary stemmer. There are a couple of reasons this should be so:
- Dictionary quality
A dictionary stemmer is only as good as its dictionary. The Oxford English Dictionary website estimates that the English language contains approximately 750,000 words (when inflections are included). Most English dictionaries available for computers contain about 10% of those.
The meaning of words changes with time. While stemming
mobilmay have made sense previously, it now conflates the idea of mobility with a mobile phone. Dictionaries need to be kept current, which is a time-consuming task. Often, by the time a dictionary has been made available, some of its entries are already out-of-date.
If a dictionary stemmer encounters a word not in its dictionary, it doesn’t know how to deal with it. An algorithmic stemmer, on the other hand, will apply the same rules as before, correctly or incorrectly.
- Size and performance
A dictionary stemmer needs to load all words, all prefixes, and all suffixes into memory. This can use a significant amount of RAM. Finding the right stem for a word is often considerably more complex than the equivalent process with an algorithmic stemmer.
Depending on the quality of the dictionary, the process of removing prefixes and suffixes may be more or less efficient. Less-efficient forms can slow the stemming process significantly.
Algorithmic stemmers, on the other hand, are usually simple, small, and fast.
If a good algorithmic stemmer exists for your language, it is usually a better choice than a dictionary-based stemmer. Languages with poor (or nonexistent) algorithmic stemmers can use the Hunspell dictionary stemmer, which we discuss in the next section.
Intro to Kibana
ELK for Logs & Metrics