What is a chatbot?
In this article, we’ll cover the basic chatbot terms that you’ll need to know before diving into further research about how a chatbot works or how to build one. But first, what are chatbots? A chatbot is an artificial intelligence software that simulates human conversation through mobile apps or social media messaging platforms like Facebook messenger or Slack. You’ve probably used chatbots before, perhaps for online shopping or to book a flight or hotel. Chatbots allow businesses to automate customer service, without having to employ a customer support representative to help each customer who reaches out with a question. For businesses that target a young audience, chatbots might even increase customer satisfaction.
Chatbot Terms for Beginners
Automatic Speech Recognition
What is automatic speech recognition for chatbots?
Automatic speech recognition is the process of taking the user’s speech as input, and using computer hardware and software techniques to identify what words were actually said.
What is an autoresponder for chatbots?
An autoresponder is an automatic reply that is triggered when a user sends their first message, or a specific keyword, to the chatbot. For example, The autoresponder feature is available on Facebook messenger. If you message a brand on Facebook messenger and get an instant reply, they are probably using autoresponder.
What is a broadcast for chatbots?
A broadcast is a message that is sent to all users in your list of chatbot users. For example, you might broadcast a message to all users who liked your company’s Facebook page.
What are chat logs?
Chat logs are past records of all spoken and typed interactions between a user and chatbot.
What are classifiers for chatbots?
Classifiers are a way to categorize user inputs into different categories. Humans naturally classify objects into sets. Pianos are instruments, t-shirts are clothing, and happy is an emotion. Similarly, chatbots break down sentences and categorize the segments, to understand the intent behind each user input.
What is a compulsory input for chatbots?
A compulsory input is information that the user must enter before moving on in the chatbot conversation. The chatbot won’t ask a different question or otherwise move forward in the conversation, until the user provides the missing information.
For example, flight number might be a compulsory for airline support chatbots. The chatbot would need to know which flight you’re booked for to be able to assist you. Another example would be order tracking number, since an online e-commerce chatbot needs to know which shipment you’d like to cancel or order again.
What is entity extraction for chatbots?
Entity extraction is an umbrella term that refers to the process of adding structure to text data so that your chatbot can read it. The chatbot uses entity extraction to identify words from user utterances, and respond accordingly. If the chatbot needs more information to complete a task, it will prompt the user for an additional entity.
What are intents for chatbots?
Intents are the purpose of a user’s input into a chatbot. For example, if a restaurant has a chatbot on their website, then a customer might use it to inquire about business hours. This intent can be expressed in different ways, such as What are your hours of operation? or What time do you open and close?
What is intent classification for chatbots?
Intent classification is the process of categorizing utterances into predefined intent groups. This is important because chatbots need to accurately match utterances to specific intents, to be able to respond, continue the conversation, and provide the right answers.
What is intent recognition for chatbots?
Intent recognition (also called intent detection or intent extraction) is the process of extracting relevant information from a user utterance, so that a chatbot can understand the intent behind it. Intent recognition is a critical natural language understanding task for intelligence user interfaces to determine what kind of support a user is looking for, and how the UI can offer help.
Natural Language Processing
What is natural language processing for chatbots?
Natural language processing is a field of artificial intelligence that specializes in a machine’s ability to recognize what is said to it, understand its meaning, determine the appropriate action, and respond with language that the user can understand. Chatbot development relies heavily on natural language processing, since chatbots mimic human conversations.
Natural Language Understanding
What is natural language understanding for chatbots?
Natural language understanding is a subfield of natural language processing that aims to understand the intended meaning of chatbot utterances. Human speech is peppered with nuances, subtleties, idioms, and mispronunciations, but natural language understanding aims to sift through these complexities of human speech, to extract the user’s intent.
What is a platform for chatbots?
The platform, also called the channel, refers to the application that hosts your chatbots. These days, you can host chatbots in most social media sites, such as Facebook messenger and Twitter direct messages.
What is semantic annotation for chatbots?
Semantic annotation is the task of annotating various concepts within text, such as people, objects, or company names, to train a chatbot. The chatbot will refer to the semantic annotation to categorize new user input, and respond accordingly. Learn more.
What are chatbot utterances?
Lastly, this is one of the most important chatbot terms that you should know. Chatbot utterances are anything that the users says or types as input into the chatbot. For example, if a user types What is the current time in Tokyo, Japan?, the entire sentence is the utterance.
If you’re curious about incorporating chatbots for your business, be sure to explore our chatbot training data services. Lionbridge offers training datasets for intent variation, intent classification, chatbot utterances, and more. Take advantage of our services to ensure that your chatbot can recognize and classify user queries, and respond with the correct answer or follow-up question.