[AI 챗봇 만들기] Understanding Entities

[다니엘쌤]

Welcome to the third module of How to Build Chatbots. In the previous module we mastered intents. This module is entirely dedicated to entities, the second key component of our dialog skill. As you know by now, intents capture the user goal. Entities allow us to capture specific values within the user utterance. You might recall this example from the previous module. You’ll notice how the chatbot is correctly detecting the intent but completely ignoring the specifics of the question. In other words, “When is your Toronto store open?” and “When is your Vancouver store open?” are the same as far as our chatbot is concerned. That’s because we haven’t defined an entity to capture that specific bit of information, namely the location of the store. So what we could do is create a location entity. Notice how we use an @ symbol rather than a # symbol for entities. We can then define multiple values for our entity, such as Toronto, Montreal, Vancouver, et cetera. One value for each store location that we have. You’ll notice that we can also define synonyms for a given entity value. These can be dictionary synonyms but are often synonyms in the sense that the user might refer to the entity value in that way. Imagine for example our Toronto store is located on Warden Avenue. People might ask about hours of operation of our Toronto store, or they might phrase it as, “What are the hours of operation of your Warden Ave store?”. Either should lead our chatbot to detect the same entity value. Synonyms are optional, but are a useful feature and worth defining when applicable. With our location entity defined, we can now detect the intent and the specific location. With these two pieces of information we can provide an appropriate and specific answer to the user. Note that although entities values can have spaces in them, we need a special syntax to represent them. If the entity value has a space in it, we simply wrap it in parentheses to refer to it when, later on, we’ll define the rules of our dialog. Much like intents, we can also import entities from CSV. In fact, in the labs for this module you’ll import them in this way and learn how to export them to CSV as well. Other than entering them manually in Watson Assistant and importing them from CSV, there is also a third way to add entities to your chatbot. In fact, there are some predefined system entities that you can enable if needed by your chatbot. As you can see there are quite a few options. @sys-currency allows us to detect mentions of currencies within the user input. @sys-date mentions of dates. For example, the user might reply to a question with “next Monday” and @sys-date will allow us to capture that piece of information as an actual specific date. @sys-person allows us to detect common names, an easy way to collect people’s names to make your chatbot more personable, as you’ll see later on in the course. System entities are quite handy and they are constantly being improved upon. Make sure you check the system entities available in your dialog skill before you jump right to implementing your own entities. Alright, now it’s your turn. By now you should know the drill. In the rest of this module, you’ll find a series of guided exercises that will teach you how to work with entities. You’ll also find some graded quizzes. You’ll also find some graded quizzes. In the next module, we’ll see how to put the intents and entities we defined to good use to provide accurate and specific responses to the user.