Our company has built chatbots for large tech retail organizations. We have done this from scratch by building out the backend infrastructure and language models, as well as the front end user experience.
We have a background in natural language processing (NLP), artificial intelligence (AI), and computer science. If one is building a chatbot from scratch, it is important to be an expert in software development concepts, as well as AI concepts of machine learning and NLP. It is also important to know about linguistics, parts of speech (nouns, verbs), and dependency parsing.
In the past few months, chatbots have made headlines and have become a much-required interface for communication. In this talk, we will discuss three aspects of chatbots:
As the amount of Unstructured Linguistic Data is increasing each day, it is becoming important to develop tools to analyze this data automatically. In this tutorial we will talk about the basics of linguistic data analytics and then build up to come more complicated pieces of NLP. We will start with basic linguistic techniques - such as Lemmatization, Part of Speech Tagging, Parsing etc, and write some code to implement some these using NLTK. Next, we will talk about how probabilities and statistics are used with Linguistic Data Processing to develop Language Models, and finally we will talk about more complicated techniques such as Deep Learning. In particular we will talk about Word2Vec, its strengths, its weaknesses and how to use it.
Dr. Rutu Mulkar-Mehta speaks at 1h 22mins: