Stemmer

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The stemmer filter reduces words to their base or root form (known as stemming), making it easier to match words with similar meanings across different inflections. The stemmer filter supports multiple languages, allowing for effective search and indexing in various linguistic contexts.

Configuration

The stemmer filter is a custom filter in Zilliz Cloud. To use it, specify "type": "stemmer" in the filter configuration, along with a language parameter to select the desired language for stemming.

Python

analyzer_params = {
    "tokenizer": "standard",
    "filter":[{
        "type": "stemmer", # Specifies the filter type as stemmer
        "language": "english", # Sets the language for stemming to English
    }],
}

Java

Map<String, Object> analyzerParams = new HashMap<>();
analyzerParams.put("tokenizer", "standard");
analyzerParams.put("filter",
        Collections.singletonList(
                new HashMap<String, Object>() {{
                    put("type", "stemmer");
                    put("language", "english");
                }}
        )
);

NodeJS

const analyzer_params = {
    "tokenizer": "standard",
    "filter":[{
        "type": "stop", // Specifies the filter type as stop
        "stop_words": ["of", "to", "_english_"], // Defines custom stop words and includes the English stop word list
    }],
};

Go

// go

cURL

# restful
analyzerParams='{
  "tokenizer": "standard",
  "filter": [
    {
      "type": "stemmer",
      "language": "english"
    }
  ]
}'

The stemmer filter accepts the following configurable parameters.

Parameter	Description
language	Specifies the language for the stemming process. Supported languages include: "arabic", "danish", "dutch", "english", "finnish", "french", "german", "greek", "hungarian", "italian", "norwegian", "portuguese", "romanian", "russian", "spanish", "swedish", "tamil", "turkish"

The stemmer filter operates on the terms generated by the tokenizer, so it must be used in combination with a tokenizer. For a list of tokenizers available in Zilliz Cloud, refer to Tokenizer Reference.

After defining analyzer_params, you can apply them to a VARCHAR field when defining a collection schema. This allows Zilliz Cloud to process the text in that field using the specified analyzer for efficient tokenization and filtering. For details, refer to Example use.

Examples

Before applying the analyzer configuration to your collection schema, verify its behavior using the run_analyzer method.

Analyzer configuration:

Python

analyzer_params = {
    "tokenizer": "standard",
    "filter":[{
        "type": "stemmer", # Specifies the filter type as stemmer
        "language": "english", # Sets the language for stemming to English
    }],
}

Java

// java

NodeJS

// javascript

Go

// go

cURL

# restful
analyzerParams='{
  "tokenizer": "standard",
  "filter": [
    {
      "type": "stemmer",
      "language": "english"
    }
  ]
}'

**Verification using run_analyzer:

Python

# Sample text to analyze
sample_text = "running runs looked ran runner"

# Run the standard analyzer with the defined configuration
result = MilvusClient.run_analyzer(sample_text, analyzer_params)
print(result)

Java

// java

NodeJS

// javascript

Go

// go

cURL

# restful
not support yet

Expected output (with language: "english"):

['run', 'run', 'look', 'ran', 'runner']