Chatbots use natural language processing (NLP) to understand the users’ intent and provide the best possible conversational service. Intent recognition is a critical feature in chatbot architecture that determines if a chatbot will succeed at fulfilling the user’s needs in sales, marketing, or customer service.
The quantity of the chatbot’s training data is key to maintaining a good conversation with the users. However, the data quality determines the bot’s ability to detect the right intent and generate the correct response.
Here, we present techniques for companies to improve their chatbots’ intent recognition to make them more efficient.
How do chatbots identify intent?
NLP allows the chatbot to understand the user’s message, and machine learning classification algorithms help it to classify this message based on the training data in order to deliver the correct response.
The steps required for the chatbot to have a meaningful conversation include:
1. Preprocessing for NLU
Natural language understanding (NLU) is a subfield in NLP that focuses on organizing the user’s unstructured input such that the chatbot can understand and analyze it. This process includes:
- Syntax analysis: Identifying the basic grammar rules, word organization, combination and relation to one another. This consists of :
- Splitting the text into smaller segments (words, shorter sentences) called “Tokens,”
- Labeling the tokens as nouns, verbs, adjectives, etc. This step is called “Part of Speech tagging (PoS).
- Reducing words into their roots for better analysis.
- Filter out filling words to save space and time in processing large data
- Semantic analysis: inferring the meaning of the input sentence by:
- distinguishing the context of each word
- understand the relationships between the words in the text.
NLU models utilize:
- Supervised machine learning for syntax analysis steps (tokenization, PoS tagging), such as support vector machines (SVM), Bayesian networks, and maximum entropy algorithms.
- Unsupervised machine learning for semantic analysis such as clustering algorithms.
For more on difference between NLU and NLP you can read our NLU vs NLP: Main Differences & Use Cases Comparison article.
2. Chatbot intent classification
The classification of customers’ potential intentions is done by classifiers. One customer would want to check the status of an order, while another customer might want to check the coupons she has, which are two different types of queries.
Classifiers are trained on relevant labeled datasets. Therefore, this is a supervised/human in the loop learning application. Classifiers utilize:
- Rules-based pattern matching
- Machine learning classification algorithms such as decision trees, naive Bayes, and logistic regression.
- Deep learning such as artificial neural networks
An intent classifier is used to match the output of the NLU process to relevant pre-defined labels in the training dataset. For example, when the user tells the chatbot: “I want to book a flight from Houston to LA”, the intent classifier will classify the context and sequence of words under the label “book flight”.
3. Response generation
To generate responses, chatbots either rely on pre-defined recommendations or they could generate recommendations on the fly.
For commercial applications, responses tend to be pre-defined to ensure that customers receive a consistent service and the bot does not respond in unintended ways leading to public relations failures.
The dialog is formulated to achieve a specific goal, such as acquiring the user’s information, providing suggestions about a product or a service, or directing the user to a live agent.
Chatbot intent types
Chatbot intent examples can vary widely and are shaped by the use case and target audience. You can create new sequences to align with specific user intents by identifying their needs and behaviors:
Navigational intent: Focuses on guiding users through a website or platform. Through this type chatbots can provide access to frequently requested information.
Informational intent: Addresses user queries by delivering relevant information. Often involves responding to requests about product details, service descriptions, or other specific data the user is seeking.
Transactional intent: Supports users in completing transactions, such as purchasing products or services.
Feedback intent: This intent is designed to collect user feedback, whether for product improvement, service optimization, or gathering user-generated content like reviews or opinions for future use.
Support intent: This intent focuses on addressing user needs for assistance, including troubleshooting issues, resolving problems, or seeking technical support related to products or services.
These chatbot intents are linked to different entities that define user needs and expectations. While additional intents and entities can be applied, these four provide a foundation for understanding chatbot interactions.
Challenges & 3 ways to overcome them
Challenges
Chatbots’ goal is to produce human-like comprehensive conversations to meet the user’s expectations. However, chatbots face limitations in understanding the user’s intent because:
- Natural language is difficult, even for humans.
- As humans, we have the ability to represent our intent using varying linguistic structures.
- Users sometimes make typos and use keywords instead of complete sentences.
- Most chatbot responses are limited to the pre-defined intents in the trained dataset.
Limitations cause chatbots to misunderstand the user’s intent, deliver wrong responses, and fail to fulfill their purpose of use. Feel free to read our article on chatbot epic fails for more details.
Best practices
However, some limitations can be addressed by integrating different solutions:
- AI spell checking algorithms can be implemented with NLP models to autocorrect users’ misspellings and typos.
- For example, Google Docs’s autocorrect feature points out misspellings, grammatical errors, and provides enhancements on text structure.
- Users can create custom intents.For example, in Amazon Alexa, the user can set rules for the chatbot to perform a specific task by providing a name and a list of utterances that users would say to invoke this intent.
- Increasing the volume of training data will decrease the margin of error in intent detection.
We expect this problem to be solved eventually by having more training data. There is already significant and increasing usage of conversational bots. Based on our statistics about chatbots,
- 23% of organizations use chatbots in administrative departments.
- 20% use them in the customer service department.
- 16% use AI chatbots and assistants in sales and marketing departments.
- 40% of millennials engage with chatbots on a daily basis.
5 examples of chatbot intent
Order processing intent: Focuses on processing orders and assisting users in selecting or managing purchases.
- Example Query: “Can you help me place an order for a laptop?”
Account management intent: Targets managing account details or accessing account-related information.
- Example Query: “How can I update my email address on my account?”
Appointment scheduling intent: This intent-based chatbot facilitates scheduling, rescheduling, or canceling appointments
- Example Query: “I want to book a doctor’s appointment for next Tuesday.”
Payment assistance intent: Handles issues related to payments, billing queries, or tracking payment statuses.
- Example Query: “Why was my payment declined for the recent purchase?”
Product inquiry intent: Responds to user queries about product availability, features, or specifications.
- Example Query: “Do you have the latest smartphone model in stock?”
For more on chatbots and NLP
If you are interested in learning more about chatbots, you may find these articles interesting as well:
- Top 12 Benefits of Chatbots: The Ultimate Guide.
- 30+ Chatbot Use cases / Applications in Business.
- Conversational Commerce: Benefits, Best Practices & Examples.
- Top 5 Conversational Commerce Examples & Success Stories.
If you are ready to deploy chatbots and want to make sure that you deploy the highest performing ones when it comes to intent detection, we have 2 lists of vendors to help you:
And if you want us to guide you to find the best chatbot tool for your business
FAQ
What is chatbot intent?
Chatbot intent refers to the purpose or goal behind a user’s interaction with an intent-based chatbot. It is how the chatbot interprets what the user wants to achieve, such as asking a question, making a purchase, or seeking support.
How does a chatbot recognize user intent?
Chatbots use natural language processing (NLP) and machine learning algorithms to analyze user queries, identify patterns, and map them to predefined intents, enabling accurate responses.
Why are intents important for chatbots?
Intents are critical for chatbots because they help understand and address user needs effectively. By defining intents, chatbots can deliver relevant responses, automate workflows, and enhance customer interactions.
How are intents and entities related?
Intents define the action or purpose behind a user query, while entities provide additional details that refine the intent. For example:
Intent: Book Appointment
Entities: Date (e.g., “next Monday”), Time (e.g., “3 PM”), Service (e.g., “doctor consultation”)
Can a chatbot handle multiple intents in one query?
Advanced chatbots with sophisticated NLP capabilities can detect and process multiple intents in a single query. For example, “Check my account balance and update my email address.”
How are chatbot intents created?
Chatbot intents are created by analyzing common customer interactions and defining specific goals or actions. These are then trained into the chatbot using sample queries and relevant data.
How do you test chatbot intents?
Testing involves using sample queries to ensure the chatbot accurately maps user input to the correct intent. Regular monitoring and updates are necessary to improve accuracy based on real user interactions.
Comments
Your email address will not be published. All fields are required.