We are living in an age where we simply need to speak to the VA (voice assistant) and command to get things done for us. This is where NLP or Natural language processing with AI comes into the picture. As the subset of machine learning and an AI component, NLP was first implemented in around 1952 as per the Hodgkin-Hexley model. While, it was Alan Turing in 1950, who first recognized that a ‘thinking machine’ should be able to interpret and understand conversations in the language spoken by humans.
As a means to form a bridge between communication for machines and humans, NLP has found diverse applications across the business landscape. Text to speech and speech-to-text has been one of the earliest ways of establishing communication through machines. With over 100 languages spoken around the world, the scope of NLP has been on an ascending trajectory with tasks like topic modeling, sentiment analysis, context extraction, text classification, named entity detection, text summarization executed with the help of natural language processing in AI applications.
Data for NLP in Artificial Intelligence
Whether it is predictive text, email filters, search assistants, language translation, and text analytics, a variety of tasks are performed using NLP methodologies in AI as data labeling, extraction, classification, and linking form an integral part of all NLP tasks. Before data can be utilized for NLP projects, it is scrubbed of unwanted elements like emoticons and arranged into fields for labeling. For instance, sometimes datasets are converted into binary labels for developing multi-domain sentiment analysis datasets.
Aside from data scrubbing, providing a structure to data is called topic modeling. It is another aspect that is crucial for dimensionality reduction, which improves the overall functioning of the machine learning model. In scenarios like data analysis of the text, classification of the text, and building a recommendation system, data scientists must be prepared to juggle a variety of data.
Natural Language Processing Use Cases
NLP is often referred to as machine translation. The most common application of AI-enabled platforms or applications with NLP at the back is across sectors like healthcare, research, and development, pharma, hospitality, e-commerce, finance to name a few.
Simultaneously, the power of NLP is being utilized in reading customer comments and other forms of textual mentions. NLP primarily focused on language interpretations, hence, comments, reviews, photos, posts, and shares, which are part of the online community today have become factors impacting business intelligence. Web and online communities are key to gathering crucial insights and data and dig into them for extracting useful trends and analysis. These trends provide a deeper insight into what online data extraction can do for any business. Not merely for analyzing how and what the customer is looking for and expecting from a brand, with data like online reviews businesses can modify their products and services range and improve customer experiences.
AI-enabled applications and bots are instrumental in understanding customer sentiments and formulate responses, accordingly. The majority of product and service websites these days are enabled with chatbots, powered with NLP. Specifically, websites enabled with Artificial Intelligence-powered chatbots can process customer conversation using NLP methodologies and gauge customer sentiments, before responding. The chatbots are also capable of directing customers to find a credible solution to their problems.
The evolution story of NLP is long yet interesting. Various forms of language tasks have resulted in the approach of what we have named as NLP or natural language processing today.
Natural language processing also finds its implication in deep learning wherein, multi-layer learning is involved. The birth of neural networks has played a key role in the creation of AI methodologies like natural language processing come into existence. As much the concept of ‘thinking machine’ has inspired NLP application, neural network model like Feed-Forward (propose in 2001) has impacted the application of NLP and widened its scope to great extents. Recurrent neural networks or RNNs and LTSM (long-short memory networks) have made use of language modeling for word embeddings, sequence-to-sequence models, and pre-trained language models.
Not merely in adding value to AI training data tasks and machine learning models, NLP has deeper roots in various deep learning activities based on artificial neural layers. Some of the milestones of which involve, character-based representation, reinforcement learning, and adversarial learning.