How to Build Your AI Chatbot with NLP in Python?

To extract the city name, you get all the named entities in the user’s statement and check which of them is a geopolitical entity (country, state, city). To do this, you loop through all the entities spaCy has extracted from the statement in the ents property, then check whether the entity label (or class) is “GPE” representing Geo-Political Entity. If it is, then you save the name of the entity (its text) in a variable called city. In this section, you will create a script that accepts a city name from the user, queries the OpenWeather API for the current weather in that city, and displays the response. In the next section, you’ll create a script to query the OpenWeather API for the current weather in a city. In this step, you will install the spaCy library that will help your chatbot understand the user’s sentences.

Each has its strengths and drawbacks, and the choice is often influenced by specific organizational needs. Generally, the “understanding” of the natural language (NLU) happens through the analysis of the text or speech input using a hierarchy of classification models. Take one of the most common natural language processing application examples — the prediction algorithm in your email.

Benefits of Chatbots using NLP

Some of the most popularly used language models are Google’s BERT and OpenAI’s GPT. These models have multidisciplinary functionalities and billions of parameters which helps to improve the chatbot and make it truly intelligent. Next, our AI needs to be able to respond to the audio signals that you gave to it. Now, it must process it and come up with suitable responses and be able to give output or response to the human speech interaction. This method ensures that the chatbot will be activated by speaking its name. NLP technologies have made it possible for machines to intelligently decipher human text and actually respond to it as well.

That way the neural network is able to make better predictions on user utterances it has never seen before. You have to train it, and it’s similar to how you would train a neural network (using epochs). In general, things like removing stop-words will shift the distribution to the left because we have fewer and fewer tokens at every preprocessing step.

Rule-Based Chatbot Development with Python

With the addition of more channels into the mix, the method of communication has also changed a little. Consumers today have learned to use voice search tools to complete a search task. Since the SEO that businesses base their marketing on depends on keywords, with voice-search, the keywords have also changed.

But designing a good chatbot UI can be as important as managing the NLP and setting up your conversation flows. You can add as many synonyms and variations of each query as you like. Just remember, each Visitor Says node that begins the conversation flow of a bot should focus on one type of user intent. On the other hand, NLG (Natural Language Generation), also a subset of NLP, enables the system to write. That is, it’s what enables the machine to respond in text in the human language. These texts can, through other systems, be converted into spoken speech.

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This URL returns the weather information (temperature, weather description, humidity, and so on) of the city and provides the result in JSON format. After that, you make a GET request to the API endpoint, store the result in a response variable, and then convert the response to a Python dictionary for easier access. Here are three key terms that will help you understand how NLP chatbots work. When you first log in to Tidio, you’ll be asked to set up your account and customize the chat widget. The widget is what your users will interact with when they talk to your chatbot.

Then I also made a function train_spacy to feed it into spaCy, which uses the nlp.update method to train my NER model. It trains it for the arbitrary number of 20 epochs, where at each epoch the training examples are shuffled beforehand. Try not to choose a number of epochs that are too high, otherwise the model might start to ‘forget’ the patterns it has already learned at earlier stages.

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