Token filters (lowercase, stemmer, synonym) in Elasticsearch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the lowercase token filter do in Elasticsearch?

The lowercase token filter converts all characters in tokens to lowercase. This helps make searches case-insensitive, so 'Apple' and 'apple' are treated the same.

beginner

Explain the purpose of a stemmer token filter.

A stemmer token filter reduces words to their root form. For example, 'running', 'runs', and 'ran' become 'run'. This helps match different forms of a word during search.

beginner

What is a synonym token filter used for?

A synonym token filter replaces words with their synonyms during indexing or searching. For example, 'quick' might be replaced with 'fast'. This expands search results to include similar meanings.

intermediate

How do token filters fit into the Elasticsearch analysis process?

Token filters process tokens after the text is split by the tokenizer. They modify tokens by changing case, stemming, or adding synonyms before indexing or searching.

intermediate

Give an example of how to define a lowercase and stemmer token filter in Elasticsearch settings.

Example settings snippet:

{
  "analysis": {
    "filter": {
      "my_lowercase": { "type": "lowercase" },
      "my_stemmer": { "type": "stemmer", "language": "english" }
    }
  }
}

What does the lowercase token filter do in Elasticsearch?

A Converts all tokens to lowercase

B Removes stop words

C Splits text into tokens

D Adds synonyms to tokens

The lowercase token filter converts all tokens to lowercase to make searches case-insensitive.

Which token filter reduces words to their root form?

A Stop filter

B Synonym filter

C Lowercase filter

D Stemmer filter

The stemmer filter reduces words to their root form, like 'running' to 'run'.

What is the main use of a synonym token filter?

A To convert tokens to lowercase

B To remove punctuation

C To replace words with their synonyms

D To split text into tokens

The synonym filter replaces words with their synonyms to expand search results.

In which part of the analysis process do token filters operate?

A After tokenizing text

B Before tokenizing text

C During indexing only

D Only during searching

Token filters modify tokens after the tokenizer splits the text.

Which of these is a correct way to define a lowercase filter in Elasticsearch settings?

A "filter": { "my_lowercase": { "type": "synonym" } }

B "filter": { "my_lowercase": { "type": "lowercase" } }

C "filter": { "my_lowercase": { "type": "stemmer" } }

D "filter": { "my_lowercase": { "type": "tokenizer" } }

The lowercase filter is defined with type "lowercase" in the filter settings.

Describe how lowercase, stemmer, and synonym token filters help improve search results in Elasticsearch.

Think about how each filter changes the words to match more queries.

Explain the order of operations in Elasticsearch analysis involving tokenizers and token filters.

Remember token filters work after tokenizing.

Practice

1.

What does the lowercase token filter do in Elasticsearch?

easy

A. Converts all tokens to lowercase letters

B. Removes stop words from the tokens

C. Finds the root form of tokens

D. Adds synonyms to tokens

Solution

1. Step 1: Understand the lowercase filter purpose

   The lowercase filter changes all tokens to lowercase to make searches case-insensitive.

2. Step 2: Compare with other filters

   Stemmer finds root words, synonym adds similar words, stop word filter removes common words. Only lowercase converts to lowercase.

3. Final Answer:

   Converts all tokens to lowercase letters -> Option A

4. Quick Check:

   Lowercase filter = converts tokens to lowercase [OK]

Hint: Lowercase filter always makes words small letters [OK]

Common Mistakes:

• Confusing lowercase with stemmer
• Thinking lowercase adds synonyms
• Mixing lowercase with stop word removal

2.

Which of the following is the correct syntax to define a synonym token filter in Elasticsearch?

{
  "filter": {
    "my_synonym": {
      "type": "synonym",
      "synonyms": ["quick,fast"]
    }
  }
}

easy

A. { "type": "stemmer", "synonyms": ["quick,fast"] }

B. { "type": "stop", "synonyms": ["quick,fast"] }

C. { "type": "synonym", "synonyms": ["quick,fast"] }

D. { "type": "lowercase", "synonyms": ["quick,fast"] }

Solution

1. Step 1: Identify the token filter type for synonyms

   The synonym filter must have "type": "synonym" to work correctly.

2. Step 2: Check the synonyms property

   The synonyms list is correctly provided as an array of strings with comma-separated words.

3. Final Answer:

   { "type": "synonym", "synonyms": ["quick,fast"] } -> Option C

4. Quick Check:

   Synonym filter syntax = type synonym with synonyms list [OK]

Hint: Synonym filter always uses type 'synonym' with synonyms array [OK]

Common Mistakes:

• Using wrong type like stemmer or lowercase
• Not providing synonyms as an array
• Misspelling 'synonym' in type

3.

Given this analyzer configuration in Elasticsearch:

{
  "analyzer": {
    "my_analyzer": {
      "tokenizer": "standard",
      "filter": ["lowercase", "porter_stem"]
    }
  }
}

What will be the output tokens for the input text "Running Runs"?

medium

A. ["run", "runs"]

B. ["running", "runs"]

C. ["Running", "Runs"]

D. ["run", "run"]

Solution

1. Step 1: Apply lowercase filter

   Input "Running Runs" becomes "running runs" after lowercase filter.

2. Step 2: Apply porter_stem filter

   Porter stemmer reduces "running" and "runs" to root "run" for both tokens.

3. Final Answer:

   ["run", "run"] -> Option D

4. Quick Check:

   Lowercase + stemmer = root forms in lowercase [OK]

Hint: Lowercase then stemmer gives root lowercase tokens [OK]

Common Mistakes:

• Ignoring stemming effect
• Not applying lowercase first
• Expecting original case tokens

4.

Identify the error in this token filter definition:

{
  "filter": {
    "my_stemmer": {
      "type": "stemmer",
      "language": "englishy"
    }
  }
}

medium

A. "type" should be "stemmer_override"

B. "language" value is misspelled; should be "english"

C. "filter" key must be inside "analyzer"

D. Missing "synonyms" array

Solution

1. Step 1: Check the "language" parameter spelling

   The language "englishy" is incorrect; the correct value is "english".

2. Step 2: Verify the stemmer type and parameters

   Type "stemmer" is correct; no synonyms needed here; filter can be defined outside analyzer.

3. Final Answer:

   "language" value is misspelled; should be "english" -> Option B

4. Quick Check:

   Stemmer language spelling must be exact [OK]

Hint: Check spelling of language names in stemmer filters [OK]

Common Mistakes:

• Using invalid language names
• Confusing stemmer with synonym filter
• Expecting synonyms in stemmer filter

5.

You want to create an analyzer that makes searches case-insensitive, finds root forms of words, and also matches synonyms like "fast" for "quick". Which token filters should you include in your analyzer?

hard

A. ["lowercase", "stemmer", "synonym"]

B. ["stop", "lowercase", "synonym"]

C. ["lowercase", "synonym"]

D. ["stemmer", "stop"]

Solution

1. Step 1: Identify filters for case-insensitivity and root forms

   Lowercase filter makes tokens lowercase; stemmer finds root forms.

2. Step 2: Add synonym filter for matching similar words

   Synonym filter adds synonyms like "fast" for "quick" to improve matching.

3. Final Answer:

   ["lowercase", "stemmer", "synonym"] -> Option A

4. Quick Check:

   Lowercase + stemmer + synonym = case-insensitive, root, and synonym matching [OK]

Hint: Combine lowercase, stemmer, and synonym filters for full matching [OK]

Common Mistakes:

• Omitting stemmer or synonym filters
• Using stop filter instead of stemmer
• Not including lowercase for case-insensitivity