Natural Language Processing (NLP) allows you to understand and extract meaningful information (intents, entities and traits) out of the messages people send. You can then use this information to identify intent, automate some of your replies, route the conversation to a human via livechat, or any other requirement your chat experience needs.
If you are currently leveraging an NLP API, you have to make an extra call when you receive the user message, which adds latency and complexity (example: async, troubleshooting, etc.) With Built-in NLP, intents and entities are automatically detected in every text message that someone sends.
Powered by Wit.ai, Built-in NLP is a simple way for you to incorporate NLP into your bot. This feature is integrated within the Workplace Platform, and automatically detects meaning and information in the text of messages that a user sends before it gets passed to the bot.
Built-in NLP detects many entities and traits from a list of defaults (such as greetings, distance, and duration) that are supported in several languages. To view the most updated list, please click here.
Currently the Built-in NLP features in Workplace are only available to Workplace Bots in Chats.
Once Workplace's built-in NLP is enabled for your Custom integration, it automatically detects meaning and intent in every text message before it is sent to your bot. The message will be relayed to your bot as usual, along with any meaningful information detected in the body. See Handling a Message With NLP.
By default, Workplace's Built-in NLP detects the same entities and traits supported by the Messenger Platform.
Dates and times are automatically localized based on the locale sent in the user's profile.
For example, if someone sends the message, “tomorrow at 2pm” or “2 days before Xmas,” you will get the actual timestamp with the message.
Not all entities and traits are available in all languages. The sentiment trait, for example, is only available in English.
By default, Workplace's Built-in NLP detects the languages supported by the Messenger Platform.
To enable Built-in NLP, do the following:
Built-in NLP also supports the following advanced settings that let you further customize the nlp object included in messages webhook events. To enable/disable these settings, click the 'Advanced Settings` button:
You can also use the graph API to enable built-in NLP programmatically:
curl -i -X POST \
"https://graph.facebook.com/v2.8/me/nlp_configs?nlp_enabled=true"
-H 'Authorization: Bearer $token' \You can disable NLP using nlp_enabled=false on the request above.
Once Built-in NLP is enabled, you will see an nlp key in the request sent to your message webhook.
For example, the message, "see you tomorrow at 2pm" would include the following information:
{...,
"entities": {
"wit$datetime:datetime": [
{
"id": "340464963587159",
"name": "wit$datetime",
"role": "datetime",
"start": 8,
"end": 23,
"body": "tomorrow at 2pm",
"confidence": 0.9648,
"entities": [],
"type": "value",
"grain": "hour",
"value": "2020-06-16T14:00:00.000-07:00",
"values": [
{
"type": "value",
"grain": "hour",
"value": "2020-06-16T14:00:00.000-07:00"
}
]
}
]
},
"traits": {
"wit$sentiment": [
{
"id": "5ac2b50a-44e4-466e-9d49-bad6bd40092c",
"value": "neutral",
"confidence": 0.6162
}
]
}
}For each message, the Workplace will return a mapping of the entities and traits that were captured alongside their structured data. The key pieces of information here are the confidence and the value for each entity or trait.
confidence is a value between 0 and 1 that indicates the probability the parser thinks its recognition is correct.
value is the parser output. For example, 2pm can be converted to an ISO string you can use in your bot, like "2017-05-10T14:00:00.000-07:00".
You can learn more about the JSON structure of all the entities and traits in the Wit.ai docs
In your messages webhook, you can update the logic used to respond to messages by taking advantage of Default NLP. For example, if you have a handleMessage() function in your webhook that responds to each message received, you can use the wit$greetings trait to send an appropriate response:
function firstTrait(nlp, name) {
return nlp && nlp.traits && nlp.entities[name] && nlp.traits[name][0];
}
function handleMessage(message) {
// check greeting is here and is confident
const greeting = firstTrait(message.nlp, 'wit$greetings');
if (greeting && greeting.confidence > 0.8) {
sendResponse('Hi there!');
} else {
// default logic
}
}Replicate this logic for other entities and traits, and you will be on your way to using Built-in NLP!
You can customize Workplace's Built-in NLP to detect additional intents, entities and traits in English, as well as in the 132 languages supported by Wit.ai.
To customize NLP with Wit.ai, do the following:
You can also 'Link to existing Wit app' and add your Wit Server access token. You can find your access token in the Wit App settings.
Use the Wit.ai getting started guide to learn how to identify and validate your custom intents, entities and traits.
You can also update your NLP settings programmatically using the Graph API.