To extract phrases & named entities from text, do an HTTP POST to http://text-processing.com/api/phrases/ with form encoded data containg the text you want to analyze. You’ll get back a JSON object containing lists of phrases and/or named entities.
Here’s an example of how to do it using curl:
$ curl -d "text=California is nice" http://text-processing.com/api/phrases/
{
"NP": ["California"],
"GPE": ["California"],
"VP": ["is"],
"LOCATION": ["California"]
}
You can try out the tagging and chunking demo to get a feel for the results and the kinds of phrases that can be extracted. English phrase extraction combines the results from 4 different phrase & named entity chunkers: the default named entity chunker, a treebank trained noun phrase chunker, a conll2000 trained phrase chunker, and an ieer trained named entity chunker.
An example for the Spanish language is:
$ curl -d "language=spanish&text=San Francisco, California" http://text-processing.com/api/phrases/
{
"LOC": ["San Francisco"]
}
text: | Required - the text you want to process. It must not exceed 1,000 characters. |
---|---|
language: | Optional, defaults to english. There are 3 other language choices for phrase extraction:
|
On success, a 200 OK response will be returned containing a JSON object that looks like this:
{
"NP": ["noun phrase"]
}
The object will have a key for each type of phrase or named entity, and the value for that key will be a list of strings, one for each phrase of that type. If no phrases or named entities could be extracted, you’ll get an empty object.
A 400 Bad Request response will be returned under the following conditions:
A 503 Throttled response will be returned if you exceed the daily request limit. Signup for the Mashape Text-Processing API to get a higher limit plan.