Into NLP 5 ~ Numerous Language Parts – POS Tagging
Last time we had a look at the task of text normalization as a way of simplifying matching and searching for certain words. I mentioned that for the more complex normalization techniques one needs additional information: Since nouns, verbs, and
Into NLP 3 ~ Numerous Language Pieces – Tokenization
Previously we started our journey into the world of Natural Language Processing by learning the basics of Regular Expressions (RegEx) and fuzzy matching. While RegEx is certainly a powerful tool, it has its problems. As I wrote, Regular Expressions have a
Into NLP 2 – Fuzzy String Matching and the Edit Distance
NearLy Perfect In my last article I started with a dive into the wonderfull world of Regular Expressions. We’ve seen how RegEx are really useful for search tasks. However… They are not perfect and today we will look at one particular
Into NLP 1 – Regular Expressions
Into the Fire - A no less somewhat less nonsense introduction to NLP
Natural Language Processing? - What is NLP?
Language is messy. In our attempts to convey meaning, and emotions to each other, we have come up with some extraordinarily complex structures that need years of learning to grasp. There are countless rules and even more exceptions to those rules but somehow we manage to communicate with each other. The name, scientists have come up with for mess is natural language.
And then there are computers, machines that require a lot of structure to work. NLP is the attempt to make those two worlds meet, to have computers parse, process, and understand the language we use in our daily (natural) lifes. In the coming articles we will have a look at tools, techniques, and methods that help us deal with the chaotic complexity of natural language. We will see the many ways in which NLP will make dealing with language easier, one method at the time. Today we will start with the first:
Regular Expressions
Our New Library for State-of-the-Art Natural Language Processing
There is a plethora of NLP libraries out there. For almost every NLP task, be it from rather trivial things like stop word removal, to more complex operations like relation extraction, there are libraries. This yields enormous power for modern
Natural Language Processing: Timeline Extraction with Regexes and spaCy
New text is generated in a mindblowing speed today. Think about news articles, social media messages, reports, e-mails etc. However, we cannot do much with unstructured text. But as soon as we extract some structured information from