What it does: Extract specific features based on a trained criteria to help structure unstructured data.
Text Example
The specs of the laptop are: Refurbished Dell Black 14"" E6420 with Intel Core i5 Processor, 6GB Memory, 320GB Hard Drive and Windows 10 Home
Automatically Labeled As:
What it does: Determining whether a text is positive, negative, or neutral
Text Example
The man was a spy of the Cairo underworld who used a trained Capuchin to track his targets or retrieve objects.
Automatically Labeled As:
What it does: Detecting the intentions behind text responses
Text Example
Ron Gilbert from LucasArts had two inspirations that led the adventures of Guybrush Threepwood. One was a novel written by Tim Powers, and the other was a ride at Disneyland.
Automatically Labeled As:
Some examples of how clients use Extractors
How to Build Custom Extractors
Easily build and train a machine learning model to recognize and extract text data.
1. Upload Data to MonkeyLearn
Create a model and import your text data by uploading files directly or by connecting with third-party apps.
2. Define Tags
Define the tags you will use for the extractor. These tags will be used to identify the pieces of text to extract.
3. Tag & Train
Train the extractor by tagging the words or phrases you’d like to extract in the text that appears. The extractor will begin to learn right before your eyes.
4. Evaluate & Improve
Test your trained model to see predicted tags. Improve the extractor with more training and by measuring precision and recall.
5. Put Your Extractor to Work
Use your new extractor to analyze new or historical texts. Either upload a file to process text in a batch, use integrations with third-party apps, or our API to extract text automatically.