Intent classification with regex I

You'll begin by implementing a very simple technique to recognize intents - looking for the presence of keywords.

A dictionary, keywords, has already been defined. It has the intents "greet", "goodbye", and "thankyou" as keys, and lists of keywords as the corresponding values. For example, keywords["greet"] is set to "["hello","hi","hey"].

Also defined is a second dictionary, responses, indicating how the bot should respond to each of these intents. It also has a default response with the key "default".

The function send_message(), along with the bot and user templates, have also already been defined. Your job in this exercise is to create a dictionary with the intents as keys and regex objects as values.

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Building Chatbots in Python

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Iterate over the keywords dictionary, using intent and keys as your iterator variables.
Use '|'.join(keys) to create regular expressions to match at least one of the keywords and pass it to re.compile() to compile the regular expressions into pattern objects. Store the result as the value of the patterns dictionary.

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# Define a dictionary of patterns
patterns = {}

# Iterate over the keywords dictionary
for ____, ____ in ____:
    # Create regular expressions and compile them into pattern objects
    patterns[intent] = ____
    
# Print the patterns
print(patterns)

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Deze oefening maakt deel uit van de cursus

Building Chatbots in Python

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In this chapter, you'll learn how to build your first chatbot. After gaining a bit of historical context, you'll set up a basic structure for receiving text and responding to users, and then learn how to add the basic elements of personality. You'll then build rule-based systems for parsing text.

Exercise 1: Introduction to conversational software Exercise 2: EchoBot I Exercise 3: EchoBot II Exercise 4: Creating a personality Exercise 5: Chitchat Exercise 6: Adding variety Exercise 7: ELIZA I: asking questions Exercise 8: Text processing with regular expressions Exercise 9: ELIZA II: Extracting key phrases Exercise 10: ELIZA III: Pronouns Exercise 11: ELIZA IV: Putting it all together

Here, you'll use machine learning to turn natural language into structured data using spaCy, scikit-learn, and rasa NLU. You'll start with a refresher on the theoretical foundations and then move onto building models using the ATIS dataset, which contains thousands of sentences from real people interacting with a flight booking system.

Exercise 1: Understanding intents and entities Exercise 2: Intent classification with regex I

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Exercise 3: Intent classification with regex II Exercise 4: Entity extraction with regex Exercise 5: Word vectors Exercise 6: word vectors with spaCy Exercise 7: Intents and classification Exercise 8: Intent classification with sklearn Exercise 9: Entity extraction Exercise 10: Using spaCy's entity recognizer Exercise 11: Assigning roles using spaCy's parser Exercise 12: Robust language understanding with rasa NLU Exercise 13: Rasa NLU Exercise 14: Data-efficient entity recognition

In this chapter, you'll build a personal assistant to help you plan a trip. It will be able to respond to questions like "are there any cheap hotels in the north of town?" by looking inside a hotel’s database for matching results.

Exercise 1: Virtual assistants and accessing data Exercise 2: SQL basics Exercise 3: SQL statements in Python Exercise 4: Exploring a DB with natural language Exercise 5: Creating queries from parameters Exercise 6: Using your custom function to find hotels Exercise 7: Creating SQL from natural language Exercise 8: Incremental slot filling and negation Exercise 9: Refining your search Exercise 10: Basic negation Exercise 11: Filtering with excluded slots

Everything you've built so far has statelessly mapped intents to actions and responses. It's amazing how far you can get with that! But to build more sophisticated bots you will always want to add some statefulness. That's what you'll do here, as you build a chatbot that helps users order coffee.

Exercise 1: Why statefulness is key Exercise 2: Form filling Exercise 3: Asking contextual questions Exercise 4: Dealing with rejection Exercise 5: Asking questions & queuing answers Exercise 6: Pending actions I Exercise 7: Pending actions II Exercise 8: Pending state transitions Exercise 9: Putting it all together I Exercise 10: Putting it all together II Exercise 11: Frontiers of dialogue research Exercise 12: Generating text with neural networks Exercise 13: Congratulations!