MLG 020 Natural Language Processing 3

This podcast episode explores the various tools and techniques used in shallow natural language processing (NLP) algorithms. The episode discusses high-level goals such as text classification, sentiment analysis, search engines, and question answering, and the tools used to achieve them, including part of speech tagging, named entity recognition, syntax parsing, and TF-IDF. The concept of data pipelines in NLP is also explained, highlighting the importance of syntax tree parsing as a pre-processing step. Additionally, the episode covers the evolution of context-free grammars (CFGs) and the role of lexicons in NLP. Dependency parsing and its application in syntax parsing are discussed, along with its use in Google search queries and question answering. The episode concludes by examining the role of NLP in question answering, text summarization, and machine translation, as well as the advancements made possible by deep learning.