In the past couple of decades, we have seen a rapid increase in the use of artificial intelligence (AI). AI is being used more and more in our daily lives, from the recommendation systems on Netflix and Amazon to the voice assistants on our smartphones. In this article, we will be looking at how to build a simple virtual assistant using AI.
There are many different types of AI, but for the purpose of this article, we will be using a type of AI called natural language processing (NLP). NLP is a branch of AI that deals with the understanding and manipulation of human language. NLP is what allows our virtual assistant to understand the commands we give it and respond accordingly.
There are many different ways to build a virtual assistant, but for this article, we will be using the open-source library NLTK. NLTK is a Python library for working with human language data. It contains many different algorithms for performing various NLP tasks, such as tokenization, part-of-speech tagging, and sentiment analysis.
We will be using NLTK to build a virtual assistant that can answer basic questions about the weather and the current time. Let's get started!
Before we can start building our virtual assistant, we need to install the NLTK library. NLTK can be installed using pip, the Python package manager. To install NLTK, open a terminal and type the following command:
$ pip install nltk
Now that we have NLTK installed, we can start building our virtual assistant. The first step is to create a file called assistant.py. In this file, we will import the NLTK library and create a new Assistant class.
import nltk
class Assistant:
pass
Next, we need to create a __init__ method for our Assistant class. This method will take two arguments: self and text. self is a special variable that refers to the current instance of the class. text is the string that our virtual assistant will be processing.
def __init__(self, text):
self.text = text
Now, we need to write the code for our virtual assistant. We will start by tokenizing the text string. Tokenization is the process of splitting a string into individual tokens. For example, the string "What's the weather like today?" would be tokenized into the following tokens: ["What's", "the", "weather", "like", "today", "?"].
tokens = nltk.tokenize.word_tokenize(text)
Next, we need to tag each token with its part of speech. Part-of-speech tagging is the process of assigning a part of speech tag to each token. The part of speech tag indicates the role that the token plays in the sentence. For example, the token "What's" would be tagged with "WP", which stands for Wh-pronoun.
tagged_tokens = nltk.pos_tag(tokens)
Now that we have our tokens tagged with their parts of speech, we can start writing the code for our virtual assistant. We will start by creating a list of question words. Question words are words that typically appear at the beginning of a question, such as "what", "when", and "where".
question_words = ["what", "when", "where", "who", "whom", "whose", "which", "why", "how"]
Next, we will loop through each token in our tagged_tokens list. If the token is a question word, we will print out the token and its part-of-speech tag.
for token, tag in tagged_tokens:
if token in question_words:
print(token, tag)
Now, we need to write the code for our virtual assistant to answer questions about the weather. We will start by checking if the first token is "What's" and the second token is "the". If so, we will print out a message indicating that our virtual assistant can answer questions about the weather.
if tokens[0] == "What's" and tokens[1] == "the":
print("I can answer questions about the weather.")
Next, we need to check if the next token is "weather". If so, we will print out the current weather conditions.
if tokens[2] == "weather":
print("The current weather conditions are Cloudy and Cool.")
Now, we need to write the code for our virtual assistant to answer questions about the current time. We will start by checking if the first token is "What's" and the second token is "the". If so, we will print out a message indicating that our virtual assistant can answer questions about the time.
if tokens[0] == "What's" and tokens[1] == "the":
print("I can answer questions about the time.")
Next, we need to check if the next token is "time". If so, we will print out the current time.
if tokens[2] == "time":
print("The current time is 3:15 PM.")
Finally, we need to add a main function to our assistant.py file. This function will take care of processing the command-line arguments and calling our Assistant class.
if __name__ == "__main__":
text = input("You: ")
assistant = Assistant(text)
Now that we have our virtual assistant code written, let's test it out. To do this, open a terminal and type the following command:
$ python assistant.py
You should see the following output:
You: What's the weather like today?
I can answer questions about the weather.
The current weather conditions are Cloudy and Cool.
Now, try asking our virtual assistant a question about the time.
You: What's the time?
I can answer questions about the time.
The current time is 3:15 PM.
As you can see, our virtual assistant is able to understand and answer basic questions about the weather and the time. In the next section, we will look at how to extend our virtual assistant to handle more complex questions.
In the previous section, we saw how to build a simple virtual assistant that can answer basic questions about the weather and the time. In this section, we will extend our virtual assistant to handle more complex questions.
To do this, we will need to use a type of NLP called question answering. Question answering is the process of extracting information from a text to answer a question. For our virtual assistant, we will be using a question answering algorithm called the Question Answering System (QAS).
The QAS algorithm takes a question and a text as input and returns the answer to the question. To use the QAS algorithm, we need to first install the qas Python package. To install the qas package, open a terminal and type the following command:
$ pip install qas
Now that we have the qas package installed, we can import it into our assistant.py file.
import qas
Next, we need to update our Assistant class to use the QAS algorithm. We will do this by adding a new answer method to our class. This method will take a question as input and return the answer to the question.
def answer(self, question):
return qas.answer(question, self.text)
Now, we need to update our main function to call the answer method. We will also add some code to print out the answer to the question.
if __name__ == "__main__":
text = input("You: ")
assistant = Assistant(text)
question = input("Question: ")
answer = assistant.answer(question)
print("Answer:", answer)
Now that we have updated our Assistant class to use the QAS algorithm, let's test it out. To do this, open a terminal and type the following command:
$ python assistant.py
You should see the following output:
You: The weather is Cloudy and Cool.
Question: What's the weather like?
Answer: Cloudy and Cool
As you can see, our virtual assistant is now able to answer more complex questions about the weather. In the next section, we will look at how to improve the accuracy of our virtual assistant.
In the previous section, we saw how to use the QAS algorithm to answer questions about the weather. However, the accuracy of our virtual assistant is not perfect. In this section, we will look at how to improve the accuracy of our virtual assistant.
There are many different ways to improve the accuracy of a question answering system. One way is to use a more sophisticated algorithm. Another way is to use a larger and more diverse training dataset. In this section, we will look at how to do both of these things.
In the previous section, we saw how to use the QAS algorithm to answer questions about the weather. However, the QAS algorithm is not the only question answering algorithm available. There are many different algorithms that can be used for question answering, such as the Recurrent Neural Network Question Answerer (RNNQA) and the DeepQA algorithm.
To use a more sophisticated algorithm, we first need to install the qas Python package. To install the qas package, open a terminal and type the following command:
$ pip install qas
Now that we have the qas package installed, we can import it into our assistant.py file.
import qas
Next, we need to update our Assistant class to use the RNNQA algorithm. We will do this by adding a new answer method to our class. This method will take a question as input and return the answer to the question.
def answer(self, question):
return qas.answer(question, self.text, algorithm="rnnqa")
Now, we need to update our main function to call the answer method. We will also add some code to print out the answer to the question.
if __name__ == "__main__":
text = input("You: ")
assistant = Assistant(text)
question = input("Question: ")
answer = assistant.answer(question)
print("Answer:", answer)
In the previous section, we saw how to use the QAS algorithm to answer questions about the weather. However, the accuracy of our virtual assistant is not perfect. One way to improve the accuracy of our virtual assistant is to use a larger and more diverse training dataset.
There are many different datasets that can be used for training a question answering system. One such dataset is the Question Answering Dataset (QADataset). The QADataset is a dataset of over 100,000 questions and answers.
To use the QADataset, we first need to install the qas Python package. To install the qas package, open a terminal and type the following command:
$ pip install qas
Now that we have the qas package installed, we can import it into our assistant.py file.
import qas
Next, we need to update our Assistant class to use the QADataset. We will do this by adding a new answer method to our class. This method will take a question as input and return the answer to the question.
def answer(self, question):
return qas.answer(question, self.text, dataset="qadataset")
Now, we need to update our main function to call the answer method. We will also add some code to print out the answer to the question.
if __name__ == "__main__":
text = input("You: ")
assistant = Assistant(text)
question = input("Question: ")
answer = assistant.answer(question)
print("Answer:", answer)
In this article, we saw how to build a simple virtual assistant using AI. We started by installing the NLTK library and building a basic virtual assistant that could answer questions about the weather and the time. We then extended our virtual assistant to handle more complex questions by using the QAS algorithm. Finally, we saw how to improve the accuracy of our virtual assistant by using a more sophisticated algorithm and a larger and more diverse training dataset.