Configuring text extraction analysis

Configure text extraction analysis by specifying tags, keywords, entity extraction models, and pattern extraction rules. Use tags and keywords to mark specific terms and their synonyms that you want to identify in the analyzed text. Text and pattern extraction models help to identify various types of named entities.

1. In the Records navigation panel, click Decision > Text Analyzer.
2. Open the Text Analyzer rule that you want to configure.
3. Perform any of the following actions:
   • To detect the most relevant words or phrases in a document to, for example, create word clouds or perform a faceted search, in the Text extraction section, select the Enable auto-tag extraction check box and perform one of the following actions:
     • To detect all significant tags in the document, click Detect all tags.
     • To detect a specific number of tags in the document, click Detect top N tag(s) and specify the number of tags that you want to detect.
   • To summarize the text that you analyze, select the Enable summarization check box and specify the compression ratio.
     Note: The compression ratio is specific to your use case. For example, to create very short summaries of large bodies of text, you can specify the compression ratio as 1% to extract only the few most information-rich sentences.
   • To extract keywords, named entities of specific types, or entities whose names follow various patterns, in the Text extraction section, select the Enable text extraction check box.
4. If you enabled the Text extraction setting, configure one of the following text extraction options:
   • To extract a set of specific keywords and their synonyms, for example, George Washington, US President, POTUS, and so on, perform the following actions:
     1. In the Keywords section, click Add keywords.
     2. In the Name field, press the Down Arrow key and select a keyword-based text extraction model.

     For more information, see Creating keyword-based text extraction models.

   • To extract named entities that belong to an open dictionary of terms, for example, names of people, locations, and so on, perform the following actions:
     1. In the Entity extraction models section, click Add model.
     2. In the Entity model field, press the Down Arrow key to select an entity extraction model.

     For more information, see Creating machine-learning text extraction models.

   • To extract account numbers, ZIP codes, case numbers, telephone numbers, and so on, perform the following actions:
     1. In the Pattern extraction models section, click Add model.
     2. In the Entity rule field, press the Down Arrow key to select a pattern extraction model

     For more information, see Creating pattern extraction models.

5. Click Save.