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You don’t have to read every single word.

Make your research faster and more effective with natural language processing

Being a scientist is a difficult path, especially if you are working for R&D in a business that expects information with no delay. Long hours in the library, reading piles of essays, and searching for a small piece of data for weeks, are typical for most researchers, also during the times of digitalization. In the times of data where the amount of information is increasing every second, every minute of your research should be effective, even though, it might give you satisfaction to screen all the data you have. You can make it smarter without losing satisfaction.

There is no doubt that in most cases the real outcome you want as a researcher is relevant, representative, and ‘just-in-time’ data that may be used as a solid base for your thesis. To avoid reading every paper on the topic you are interested in, you can use natural language processing solutions that will screen every publication you need and find relevant data. Just type as many keywords as you need, and an automated tool will bring you results. It may also help you to find other texts that you could skip during the initial research and non-obvious information that may positively influence your work and let you better ‘connect the dots.

This does not mean that you will become redundant. Don’t worry. Natural language processing will not replace you as a scientist. The most important work will still be done by you, including matching all of the elements and creating recommendations, predictions, or another desirable outcome. Automation tools are here to help you not to lose important information and to better manage the data you need for your research.

The important thing here is that AI models based on natural language processing and machine learning can learn with every new data input. Therefore, making more and more research will make your assistant more effective and less prone to errors and mistakes, that are typical for human beings.

Imagine a scenario. You and your team are working on difficult research that has been ‘contracted’ by the public authority. The outcome of the research is expected to be revealed in days and your team has not found the right answers yet. You have three options:

  1. Apologize for the delay and ask for additional time,

  2. Spend a few nights in the laboratory or office with your team trying to screen as much data as you can – tiredness is, however, one of the most common reasons for human errors,

  3. Use the automated tool for data extraction and let your people focus on relevant and important data.

The choice is yours but not forget that every hour counts, also in terms of budget and operations. Automation may be the right answer, even though, you will have to spend some time on ‘teaching’ the model that is a crucial part of the implementation of AI-like models. Again, don’t worry – the effort taken will bring positive results in seconds, not days.

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