Search relevance is a difficult problem in information retrieval. How do you ensure that you get the best results back from searching a collection of documents? Let's explore a few basic strategies, including simple searching, term-frequency searching, and TF-IDF searching.
Natural language processing is a complex and evolving field. Part computer science, part linguistics, part statistics--it can be a challenge deciding where to begin. One starting place is to look at the most influential papers in academic literature--if you can master these papers, then you'll be well on the path to becoming an NLP expert.
One of the great things about using Python for natural language processing (NLP) is the large ecosystem of tools and libraries. From tokenization, to machine learning, to data visualization--Python has something for every NLP task in your workflow. Of course, choosing the right tool isn't always so easy.