25 NLP tasks at a glance . Undoubtedly Natural Language Processing by Mirantha Jayathilaka, PhD
The division of tasks and categories could have been done in multiple other ways. I omitted the deeper details, but provided links to extra information where possible. If you have improvements, you can send add them below or you can contact me on LinkedIn.
NLP has been continuously developing for some time now, and it has already achieved incredible results. It is now used in a variety of applications and makes our lives much more comfortable. This article will describe the benefits of natural language processing. We will provide a couple of examples of NLP use cases and tell you about its most remarkable achievements, future trends, and the challenges it faces.
Some common roles in Natural Language Processing (NLP) include:
They are truly breathtaking, and they are becoming more and more complex every year. They can do many different things, like dancing, jumping, carrying heavy objects, etc. According to the Turing test, a machine is deemed to be smart if, during a conversation, it cannot be distinguished from a human, and so far, several passed this test. All these programs use question answering techniques to make a conversation as close to human as possible.
AI machine learning NLP applications have been largely built for the most common, widely used languages. And it’s downright amazing at how accurate translation systems have become. However, many languages, especially those spoken by people with less access to technology often go overlooked and under processed. For example, by some estimations, (depending on language vs. dialect) there are over 3,000 languages in Africa, alone.
Get a grip on the Natural Language Processing landscape! Start your NLP journey with this Periodic Table of 80+ NLP tasks
NLP scientists will try to create models with even better performance and more capabilities. Language modeling refers to predicting the probability of a sequence of words staying together. In layman’s terms, language modeling tries to determine how likely it is that certain words stand nearby. This approach is handy in spelling correction, text summarization, handwriting analysis, machine translation, etc. Remember how Gmail or Google Docs offers you words to finish your sentence? Challenges in natural language processing frequently involve speech recognition, natural-language understanding, and natural-language generation.
After 1980, NLP introduced machine learning algorithms for language processing. SaaS text analysis platforms, like MonkeyLearn, allow users to train their own machine learning NLP models, often in just a few steps, which can greatly ease many of the NLP processing limitations above. Artificial intelligence has become part of our everyday lives – Alexa and Siri, text and email autocorrect, customer service chatbots.
Harness the Power of ChatGPT to Uncover Insights from Your Own Data
Machine learning requires A LOT of data to function to its outer limits – billions of pieces of training data. The more data NLP models are trained on, the smarter they become. That said, data (and human language!) is only growing by the day, as are new machine learning techniques and custom algorithms. All of the problems above will require more research and new techniques in order to improve on them. Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. Till the year 1980, natural language processing systems were based on complex sets of hand-written rules.
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