Answer Server 25.3
Answer Server Help

# Customize Entity Extraction

The Passage Extractor entity extraction file provides Answer Server with a map to specify what components to use to extract entities, depending on the question classification.

When you ask a question, Passage Extractor classifies it by using the question classifier, and then finds matching documents and document sections in the data store. It uses Content component highlighting to find the most relevant passages, which it uses as candidate answers. Passage Extractor then uses Named Entity Recognition and an Agentstore component to find entities in the candidate answers that match the question classification.

For example, if you have an Agent entity database with the names of plants, and you send a question that Passage Extractor classifies as plants, Passage Extractor uses the Agentstore component to find the relevant plant entities in the candidate answer text.

By default, if you configure an Agentstore component, Passage Extractor uses the Agentstore for the classifications `HUM:gr`, all `LOC` classifications, `ENTY:plant`, `ENTY:animal`, and `ENTY:lang`. It uses Named Entity Recognition and Agentstore for the `HUM:ind` question classification, and Named Entity Recognition only for all other question classifications.

You can use the Entity Extraction file to modify these classifications, for example if you create additional Agent entity files for your data.

NOTE: You do not need to specify an entity type to extract for every question classification. If a question classification does not appear in the entity extraction file, Passage Extractor does not attempt to extract entities. This might be appropriate for many question classifications (for example, if the appropriate answer is a long description, there might not be a corresponding entity).

Passage Extractor also attempts to corroborate the candidate answers, by comparing how often particular entities occur. In most cases, this improves the quality of the result answers.

In some cases, corroboration might not be appropriate. For example, if valid answers include very common words (such as one and two), the words might occur in multiple places, and be falsely corroborated as a likely answer. For this reason, corroboration is turned off for the `NUM:count` entity type in the default entity extraction JSON file.

You might also want to turn corroboration off if likely answers occur only once in your data set. In these cases you can modify the entity extraction JSON file to turn corroboration off for particular entities.

## The Entity Extraction File Format

The entity extraction file contains the question classifications, which match the values that you use in the classifier training file. For each question classification, it also contains at least one of:

- a list of Named Entity Recognition entities that Passage Extractor must use to find entities for the question classification.
- a list of Agentstore databases that Passage Extractor must query to find entities for the question classification.

When there is an Agentstore database, you can also specify Agent `FieldText` to use in a query to the Agentstore entity database for the question classification.

The entity extraction file is a JSON file, with the following structure:

    {
       "entity_map": [
          {
             "entity_type": "QuestionClass1",
             "agentstore": {
                "databases": [ListOfAgentstoreDatabases],
                "fieldtext": "FieldTextRestriction"
             },
             "eduction": {"entities": [ListOfEductionEntities]},
             "corroborate": Boolean
          },
          {
             "entity_type": "QuestionClass2",
             "agentstore": {
                "databases": [ListOfAgentstoreDatabases],
                "fieldtext": "FieldTextRestriction"
             },
             "eduction": {"entities": [ListOfEductionEntities]},
             "corroborate": Boolean
          }
          ...
       ]
    }

where,

`QuestionClassN` | is the name of the question classification (for example, `HUM:ind`).
`ListofEductionEntities` | is an array of relevant Named Entity Recognition entities.
`ListOfAgentstoreDatabases` | is an array of databases in the Agentstore component that contain relevant entities.
`FieldTextRestriction` | is a Content component `FieldText` expression to use to restrict the Agent query in the specified database.

You must specify at least one of the `eduction` or `agentstore` properties for each question classification. If you specify the `agentstore` property, the `database` property is required, but `fieldtext` is not.

If you do not want to use entity extraction for a particular question classification, do not include it in the entity extraction file.

The `corroborate` property is optional. The default value is `true`.

The following example gives some of the question classifications in the default entity extraction file:

    {
       "entity_map": [
          {
             "entity_type": "HUM:ind",
             "agentstore": {"databases": ["people"]},
             "eduction": {"entities": ["hum/ind"]}
          },
          {
             "entity_type": "NUM:date",
             "eduction": {"entities": ["num/date", "date/*"]}
          },
          {
             "entity_type": "ENTY:plant",
             "agentstore": {
                "databases": ["organisms"],
                "fieldtext": "MATCH{PLANTAE,VIRIDIPLANTAE}:ORGANISMS_KINGDOM"
             }
          },
          {
             "entity_type": "NUM:count",
             "eduction": {"entities": ["num/count"]},
             "corroborate": false
          },

    ...

## Modify the Entity Extraction File

The default entity extraction file, included in your Answer Server installation , is appropriate for most installation. However, you might need to modify the file if:

- you do not want to configure an Agentstore component for entity extraction, and want to use Named Entity Recognition entity extraction for those question classifications instead.
- you create additional Agent entity files and index them into your Passage Extractor Agentstore.
- you create custom Named Entity Recognition entities that you want to use for entity extraction.
- you want to define entity extraction for additional question classifications
- you want to turn off corroboration for some question classifications.

To update the entity extraction file

1. Open the entity extraction JSON file in a text editor.

2. Make the necessary modifications. You can add, delete, or update, any of details for the question classifications.

   To turn off corroboration, add the `corroborate` property in a particular group and set it to `false`. For example:

       {
          "entity_type": "NUM:count",
          "eduction": {"entities": ["num/count"]},
          "corroborate": false
       }

3. Save and close the entity extraction file.

4. Restart Answer Server for your changes to take effect.

   NOTE: If you add new question classifications that do not exist in the classifier training file, you must also update the classifier training file and retrain the classifier. See Train Passage Extractor Classifiers.

## Use a Different Entity Extraction File

You can use the EntityExtractionFile configuration parameter to configure the location of the entity extraction file. If you want to move or rename the entity extraction file, or use a different file for any reason, you must modify the value of this parameter to specify the name and location of the new file.

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Version 25.3 | Last updated July 2025

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Product: Knowledge Discovery Answer Server 25.3

Topic Title: Customize Entity Extraction