Natural language processing (NLP) is an offshoot of artificial intelligence (AI) that helps the system perceive ‘natural language’ that's utilized by humans. The way humans speak and the intent or emotion behind a statement is something machines find difficult to understand. That is where NLP comes into play. It understands the basic techniques of word definition, phrases, sentences, and texts, as well as syntactic (knowledge of word meanings and vocabulary) and semantic processing (understanding the combination of phrases). It also develops applications such as machine translation (MT), question-answering (QA) ), data retrieval, discussion, document production, and recommendation programs. This is why NLP plays an integral role in helping search engines, customer support systems, business intelligence, and speaking assistants. Alexa, Siri and Google homes are a few examples of AI that use NLP.
A little background on NLP
Traces of NLP go as far as the 1950s where rule-based methods were used to build NLP systems. It included word/sentence analysis, question-answering, and machine translation. These NLP systems were built by experts who were well versed in Algorithms to build the rules. In the 1990s, with the expeditious development of the web, an abundance of knowledge became accessible that sanctioned statistical learning methods to work on NLP tasks. These statistical learning methods created magnificent strides to improve NLP. Statistical learning learns from a specific dataset and describes its features. In 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. Currently, 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 the tasks such as MT, machine reading comprehension (MRC), chatbot, etc considerably easier.
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 itself.
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 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, it started with something as 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 tasks similar to this. With even more data NLP evolved from statistical learning to deep learning which helped in getting more accurate results. Models were created to improve the efficiency of NLP tasks and now we are at a stage where we even fuse certain models to collaborate with each other to create new avenues. NLP is only getting better and improve its accuracy at a frightening speed. This is the future of the digital world.