It is a retrieval-based chatbot using deep learning techniques. A retrieval-based chatbot uses predefined input patterns and responses. It then uses some type of heuristic approach to select the appropriate response.
The chatbot is trained on the dataset which contains categories (intents), pattern and responses. It uses a special recurrent neural network (LSTM) to classify which category the user’s message belongs to and then we will give a random response from the list of responses. Created using majorly NLTK, Keras, Python.
- Tkinter- To create Graphical User interfaces (GUIs)
- Keras- Keras is a neural networks library written in Python that is high-level in nature and makes it extremely simple and intuitive to use. It works as a wrapper to low-level libraries like TensorFlow or Theano high-level neural networks library.
- SGD- class that implements the stochastic gradient descent optimizer an iterative method for optimizing an objective function with suitable smoothness properties.
- Nltk- NLTK is a standard python library with prebuilt functions and utilities for the ease of use and implementation. It provides us various text processing libraries with a lot of test datasets.
- WordNetLemmatizer - Lemmatization in NLTK is the algorithmic process of finding the lemma of a word depending on its meaning and context.
- Json- Normally the JSON functions are used to read and write directly from JSON files. The data file is in JSON format so we used the json package to parse the JSON file into Python.
- Pickle- Pickling is a way to convert a python object (list, dict, etc.) into a character stream.
- Random- Random module is an in-built module of Python which is used to generate random numbers. It generates a random float uniformly in the semi-open range 0 to 1.
- NumPy- Stands for Numerical Python. Using NumPy, mathematical and logical operations on arrays can be performed.
