A Way of Making Unstructured Data Structured

Doctor using laptop and electronic medical record (EMR) system. Digital database of patient’s health care and personal information on computer screen. Hand on mouse and typing with keyboard.

What happens when you upload a set of data, or speech, or any document for that matter into a computer? Is the computer instantly able to read it? Often the answer is no. This means people who access the data in the future may have a hard time understanding what the data in question contains. Luckily this problem is very simple to fix thanks to the advent of modern data processing technology. 

In order to turn your unprocessed and unstructured data into something that easily readable and searchable by human and computer alike a method called entity extraction is utilized. This method takes the unstructured data and identifies things such as time, date, people, places, organizations, etc. These are known as entities. Through the use of this technology, data becomes easily readable – it is the reason why when you read an article or some sort of post that you can often find a list of entity categories (people, places, organizations, etc.) and under them find a list of entities that the data contains. 

But how does this technology work? The most basic form of such extraction is of course a person manually sifting through data to identity entities within a particular document. Obviously, this is both tedious and not at all feasible given how much data is uploaded to computers and the internet on a daily basis (even on an hourly basis the amount of data is extraordinarily large). To automate this process information extraction is utilized – a sub task of which happens to be named-entity recognition (NER) which is another name for the method of structuring unstructured data. NER technology must be able to differentiate between languages and context in order to identity entities properly. Let’s consider the following example of why context is important: 

Orange Oranges Grown In Orange County 

Clearly a system cannot be solely based upon a set of just words as how could it then differentiate between the color orange, the fruit orange, or Orange county Florida? The computer must therefore be able to understand the context in which the words are being utilized. Complex programming goes into the technology necessary to extract entities from text. There are a number of approaches that can be utilized to carry out an extraction and often the approaches are combined to yield the best results. Beyond just extracting information, NER technology is able to reveal the relationships between different entities, cross-reference entities, and extract question-answer formulations in a set of data. 

This technology continues to develop and improve as time passes – there are still a number of issues that prove to be quite problematic at times. Namely, the inability of computers to fully understand syntax, linguistics, and break through language barriers. However, the solution seems to be on the horizon. Much like there are many robots and chat-bots that are able to learn based off of interactions with actual humans, this technology is benefiting from crowd-sourced human input. Thus we are quite literally contributing to the program’s knowledge bank and providing it with the nuances of our languages, dialects, and even typos. Knowledge that makes it ever more possible for a computer to make unstructured data structured.

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