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The Deep NLP: A Giant Leap for the Data Sciences Industry

The Deep NLP: A Giant Leap for the Data Sciences Industry
Ved Parkash Pati - Digital Expert | June 6, 2019
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In our previous blog post on the future of data sciences, we touched upon how deep learning methodologies will amplify natural language processing (NLP). In this post, we will explore how a deep learning model will enable the provision of exceptional results in a slew of tasks such as syntax, semantics, and pragmatic recognition and analysis.

NLU: A Step Ahead of NLP

Currently, deep learning has revolutionized the field of NLP by delivering state-of-the-art outcomes in every NLP-related task. Shallow, two-layer neural networks that are capable of reconstructing linguistic contexts of words are pioneering the delivery of improved results in NLP. Word embeddings adept at sentiment analysis will not only smarten the entire gamut of NLP but also aid the migration of traditional feed forward neural networks to recurrent ones.

At its sustained level of maturity, we hope recognition of people and places, textual translation, abstract summarization, and phrasal tagging of parts of speech will be a possibility, just like parsing is today.

The industry is making a lot of headway in this area. Consider Facebook’s Research Lab release, an artificial intelligence (AI)-enabled pre-trained vector in 294 languages. This is indicative of premature advances in NLP as the industry migrates toward natural language understanding (NLU) and ultimately along the lines of natural language generation (NLG). One can only imagine what sustained technological advances in this field will be like.

What’s the Weather Like: A Case in Point

An NLP-powered chatbot at its present level of advancement can hardly predict accurate weather conditions. NLU, on the other hand, will weigh in all possible weather determinants, such as atmospheric temperature and pressure alongside wind speed and direction and then attempt at answering.

Response generation is certainly a giant leap from current practices of information retrieval. A major departure from where technology stands today, an NLU-powered chatbot will be precise. An NLU chatbot will let you know that there will be rainfall, say on Wednesday and Thursday, with precipitation around 20% and 40% respectively and temperature of about 25 degrees on both days, no rainfall on the other days of the week with a temperature around 32 degrees.

Beyond Syntax: Into the Realm of Sentiments

Not just syntax, semantics, and pragmatic recognition, a deep learning model can also help in sentiment analysis. In this regard, neural networks can be implemented to compute the belongingness of labels. Admittedly, mining subjective texts, opinions, and sentiments to comprehend feelings is a major challenge. However, deep learning with its deep neural networks and convolutional neural networks can be leveraged to carry out a host of tasks. In effect, efforts in sentiment analysis can improve the overall game for textual analysis, visual analysis, and product review analysis.

At its sustained level of maturity, we hope that the recognition of people and places, textual translation, abstract summarization, and phrasal tagging of parts of speech will be a possibility, just like parsing is today. With significant developments in long short term memory (LSTM) networks, training a recurrent neural network to perform the aforementioned tasks will be rather easy. Though currently, it has its own set of shortcomings, Google Translate recently amped up its machine translation techniques through LSTM,i and that is indicative of a brighter future in this field.

Unlocking the Business Value

The business potential for deep NLP is immense. Its real-life applications include enhancing support work in call centers and improving results for FAQs while provisioning overall multi-lingual support. It could even yield better results in analyzing worldwide stock market predictions. Microsoft recently shared a use case where they had developed a model to predict performance of companies which were invested in by a financial services partner. They also trained a deep learning model on earning releases in textual lines while drumming up insights on investment decisions. Not only did they end up creating a model that could do a preliminary financial review but also one that changed the landscape of NLP.

HCL has been an expert player in the NLP space. Our NLP chatbot for intelligent operations and unified end-user experience is AI-powered and leverages enterprise-grade NLP and machine learning (ML). Capable of interacting through chat and voice mediums, we have over 600 plus enterprise-ready multi-industry use cases along with a cognitive console for enabling powerful integrations. Supporting collaborations across a host of ecosystem platforms like Alexa, Google, Facebook Messenger, and Skype, this solution has proven its mettle by lending agility, improving business productivity, and providing 100% response consistency to several clients worldwide.

So come, embrace HCL Digital Process Operations and take a big first step in your NLP voyage.


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