A Guide to Natural Language Processing - Federico Tomassetti - Software Architect

Natural Language Processing (NLP) comprises a set of techniques that can be used to achieve many different objectives. Take a look at the following table to figure out which technique can solve your particular problem. We are going to talk about parsing in the general sense of analyzing a document and extracting its meaning. So, we are going to talk about actual parsing of natural languages, but we will spend most of the time on other techniques. When it comes to understanding programming languages parsing is the way to go, however you can pick specific alternatives for natural languages. In other words, we are mostly going to talk about what you would use instead of parsing, to accomplish your goals. For instance, if you wanted to find all for statements a programming language file, you would parse it and then count the number of for. Instead, you are probably going to use something like stemming to find all mentions of cats in a natural language document. This is necessary because the theory behind the parsing of natural languages might be the same one that is behind the parsing of programming languages, however the practice is very dissimilar. In fact, you are not going to build a parser for a natural language. That is unless you work in artificial intelligence or as researcher. You are even rarely going to use one. Rather you are going to find an algorithm that work a simplified model of the document that can only solve your specific problem. In short, you are going to find tricks to avoid to actually having to parse a natural language. That is why this area of computer science is usually called natural language processing rather than natural language parsing. Now check your email to confirm your subscription. There was an error submitting your subscription. I'd like to learn more about NLP and language engineering We are going to see specific solutions to each problem. Mind you that these specific solutions can be quite complex themselves. The more advanced they are, the less they rely on simple algorithms. Usually they need a vast database of data about the language. A logical consequence of this is that it is rarely easy to adopt a tool for one language to be used for another one. Or rather, the tool might work with few adaptations, but to build the database would require a lot of investment. So, for example, you would probably find a ready to use tool to create a summary of an English text, but maybe not one for an Italian one.