Configuring Language Analyzersedit

While the language analyzers can be used out of the box without any configuration, most of them do allow you to control aspects of their behavior, specifically:

Stem-word exclusion

Imagine, for instance, that users searching for the “World Health Organization” are instead getting results for “organ health.” The reason for this confusion is that both “organ” and “organization” are stemmed to the same root word: organ. Often this isn’t a problem, but in this particular collection of documents, this leads to confusing results. We would like to prevent the words organization and organizations from being stemmed.

Custom stopwords

The default list of stopwords used in English are as follows:

a, an, and, are, as, at, be, but, by, for, if, in, into, is, it,
no, not, of, on, or, such, that, the, their, then, there, these,
they, this, to, was, will, with

The unusual thing about no and not is that they invert the meaning of the words that follow them. Perhaps we decide that these two words are important and that we shouldn’t treat them as stopwords.

To customize the behavior of the english analyzer, we need to create a custom analyzer that uses the english analyzer as its base but adds some configuration:

PUT /my_index
  "settings": {
    "analysis": {
      "analyzer": {
        "my_english": {
          "type": "english",
          "stem_exclusion": [ "organization", "organizations" ], 
          "stopwords": [ 
            "a", "an", "and", "are", "as", "at", "be", "but", "by", "for",
            "if", "in", "into", "is", "it", "of", "on", "or", "such", "that",
            "the", "their", "then", "there", "these", "they", "this", "to",
            "was", "will", "with"

GET /my_index/_analyze?analyzer=my_english 
The World Health Organization does not sell organs.

Prevents organization and organizations from being stemmed

Specifies a custom list of stopwords

Emits tokens world, health, organization, does, not, sell, organ

We discuss stemming and stopwords in much more detail in Reducing Words to Their Root Form and Stopwords: Performance Versus Precision, respectively.