The Evolution of NLP

NLP is constantly evolving and impacting our world in a massive way. It started limited and rudimentary as rule-based methods. With an increase in data, it progressed to statistical learning which was used in simple question-answering, predictive text and more. NLP has been evolving and growing ever since, and now we are at a stage where we even fuse models to collaborate with each other. NLP is only getting better and improving its accuracy at a frightening speed. This is the future of the digital world.
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Ananya Avasthi
PUBLISHED ON
March 24, 2022
PUBLISHED ON
March 24, 2022
October 5, 2021
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The human language is incredibly complex and machines find it difficult to understand things like intent, emotion, and nuance. Enter NLP. Natural language processing (NLP) is an offshoot of artificial intelligence (AI) that helps the system perceive ‘natural language’ that humans use. NLP narrows the communication gap between humans and computers. It understands the basic techniques of word definition, phrases, and sentences. It also understand syntactic (knowledge of word meanings and vocabulary) and semantic processing (understanding the combination of phrases). It can be used to develop applications such as machine translation (MT), question-answering (QA), and recommendation programs as used by companies like Netflix. This is why NLP plays an integral role in helping search engines, customer support systems, business intelligence, and speaking assistants like Alexa and Siri.


The Timeline of NLP Evolution

1950s: The first traces of NLP came in the 1950s, when rule-based methods were used to build NLP systems. These were primarily focused on Machine Translation, and came into high demand as a result of World War II and the need for effective translations. Uses included word/sentence analysis, question-answering, and machine translation.

1980s: Computational grammar became an active field of research. Grammar tools and resources became more available and in demand.

1990s: The 1990s saw a booming development of the web and a new interest in artificial intelligence. This created an abundance of knowledge and drove statistical learning methods to work on NLP tasks. Statistical learning learns from a specific dataset and describes its features.

2012: Deep Learning took over statistical learning, producing drastic improvements in the NLP system. Deep Learning deep dives into raw data and learns its attributes.

Current day: There's a huge demand for machines that can talk and understand our needs, and NLP is the key to that door. Just look at products like Alexa and chatbots. The neural network-based NLP (referred to as ‘neural NLP’) framework has achieved new levels of quality and has become the governing approach for NLP. Deep learning makes tasks such as MT, machine reading comprehension (MRC), chatbot, etc. considerably easier. There are so many important use cases of NLP in the world now.


New practices are that currently being used in NLP

1. Supervised learning and unsupervised learning collaboration

Supervised learning is a system used by NLP that helps determine the best mapping function between known data input and expected known output. Unsupervised learning models are equipped with the intelligence and automation to work on their own and automatically discover information, structure, and patterns from the data.

The combination of each supervised and unsupervised learning provides large support to the linguistic communication process. For instance, text analytics fuses unsupervised and supervised learning to grasp technical terms during a document and their elements of speech whereas unsupervised learning is committed to finding a mutual relationship between them.


2. Training NLP models with reinforcement learning

Reinforcement learning uses a game-like situation to the AI which employs trial and error to come up with a solution to a problem. It trains the AI to make a sequence of decisions. In order to get the system to do what the programmer wants, the AI is either rewarded or penalized based on its performance. RL models from scratch are still comparatively very slow and unstable. Therefore, rather than doing everything from scratch, data specialists will first train the NLP based on supervised models and then gauge the model using Reinforcement learning.

3. Market intelligence monitoring

NLP will plays, and will continue to play, a major role in monitoring the market by unsheathing key information for businesses to build future strategies. In 2021, NLP will find its implementation in a torrent of business domains. Market intelligence monitoring is currently being widely used in the finance sector. NLP shares exhaustive insights into market sentiments, tender delays, and closings, and extracts information from large repositories. All this information can help companies not only plan future strategies to market their brand but also gives a deep insight into their standing in the current market. It won’t be long before we start seeing these tools being applied in the medical industry as well as many others.

4. Seamlessly fine-tuning new models

Transfer learning can produce a path for pre-trained models which will be applied in sentiment analysis, text classifications, and so on. Transfer learning is employed in machine learning which is the recycling of a pre-trained model in a new situation. In transfer learning, the machine utilizes the information gained from a previous task to improvise and refine the text.

The evolution of NLP has come a long way, and it has a wild and exciting road ahead. Right now, it's getting better and improving its accuracy at a frightening speed. This is the future of the digital world, and it's an exciting forecast to look at. If you’d like to talk more about NLP or how NLP labeling could help you, feel free to reach out to info@datasaur.ai. We'd love to chat!


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