As artificial intelligence and machine learning continue to transform the way we interact with the world, the one subtle or the invisible power that we all owe a huge credit is Natural Language Processing (NLP).
From grammar check to auto-prediction, the technology the has proven to be a powerful tool in improving our lives on a day-to-day basis and is making our daily chores a lot easier to handle. In this article, we look at how NLP is aiding this process and understand how exactly is it doing so.
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One of the most common use-cases of NLP that we encounter every day is when we compose a mail in Gmail. The power of the technology is such that Gmail auto predicts the commonly used phrases while writing the mail, which includes customary addressing, greetings and a person’s name if we replaying in an email thread.
In the case of Gmail, the feature was added in 2018, when Google announced that it will use NLP for its email platform. Called the Smart Compose feature for Gmail, the tech giant relied on a combination of NLP features like Bag of Words (BoW), Recurrent Neural Network (RNN) and n-grams to device technology. This they say is to make the process of email composing easier and faster, thus helping people to concentrate on their other work.
In order to achieve this, the company used its vast pool of data to train the models and used full TPUv2 Pod to perform the experiment, thus helping the models to convergence in less than a day. The company further stated that in the future, it hopes to personalise the technology according to the individual’s style of writing.
In the hustle-bustle of everyday work chaos, it is most likely that we will misplace, misspell or even miss out words or grammar when we are composing a mail or writing something. But, thanks to the advancing capabilities of NLP, most often we are warned by the red or blue underlines that force us to look into the draft again.
In India, especially, where English is still a second-language, scores of people rely on software like Grammarly to fix their grammar and to polish their writing. The platform is known to extensively use technologies like AI, ML and NLP to make this happen. According to a blog spot by Grammarly, it said that their systems combine machine learning with a variety of natural language processing approaches. From grammatical sentences, sentences, paragraph to full text, its algorithms’ parse through many nuanced aspects to predict the outcomes.
Their AI systems have also been trained to learn from feedback, which includes certain command as ignore, “When lots of users hit “ignore” on a particular suggestion, for example, Grammarly’s computational linguists and researchers make adjustments to the algorithms behind that suggestion to make it more accurate and helpful,” it said.
Chatbots have literally replaced human service across the business from food delivery to ticket booking app and it is known for its quick and prompt to customers queries.
Essentially, NLP is leveraged at the core of the technology along Natural Language Generation (NLG), ML and Fundamental Learning (FL) to train the bots to interact with the customers and to streamline responses. By applying key features like semantics and cognitive computing, the bots are equipped to comprehend words in a particular context and respond accordingly.
The presence of different languages and variations in dialects have made India the perfect market for NLP service providers to grow and thrive. Due to this reason, the number of product offerings from technology giants in Indian regional languages has increased in the past few years as the number of Indian language users has grown to 234 million.
This has been made possible as tech giants now rely on deep neural networks for translating words or phrases into its intended meaning.
As per a Stanford study, which looked into how machine translation was conducted, the researchers used deep source-side linguistics analysis to improve their Chinese-to-English translation process and trained the classifier to recognise certain Chines words into its syntactic and semantic context. Further, they used Minimum Error Rate Training (MERT), a procedure that optimises the system’s performance on an automated measure of translation quality