April 18, 2019
What we communicate – verbal or written – carries a significant amount of information like including the tone, wording, and the context. These different aspects can be extracted and machine-interpreted to understand and predict human behavior thanks to the advances in Natural Language Processing (NLP).
NLP, a field of Artificial Intelligence (AI) gives machines the ability to read, understand and derive meaning from human languages, starting with interpreting text or speech based on the keywords. This approach has been superseded by the “cognitive” approach which focuses on getting the meaning of the words and the context by which they are being used.
Is NLP a must for business in the modern age?
The volume of market data is growing exponentially of which 80% is Text data. NLP extracts insights from text data and integrating NLP will be a significant competitive advantage to any business that would benefit by understanding its consumers.
NLP is a smart and practical approach to make education elegant and exciting. It provides a solution for various fields associated with the social and cultural context of language learning. It is an effective approach for teachers, students, authors and educators for assisting with writing, analysis, and assessment procedures. NLP powered education platforms can give personalized learning experience along with real-time help 24×7 through chatbots. Curio Learn is an example of such a smart education platform powered by NLP.
Hence, Education + NLP = Smart Education -> Intelligent Future
The new-age customer service is being redefined by NLP. It provides three distinct advantages in customer service:
To sum up, Customer Service + NLP = Happy Customers.
With rising consumers’ voicing due to social media, it is imperative to effectively manage and monitor brand mention and reputation, which can be possible with NLP using sentiment analysis, which can:
NLP also identifies the hashtags being mentioned across social media and calculates both polarity and mood to strategize a brand, product, or a service based on users’ comments.
Basically, Reputation Management + NLP = Strong & Positive Brand.
Evidently, Market Research + NLP = Market Driven Strategy.
NLP for developers
Natural Language Processing has gained significant momentum due to the substantial contribution of deep learning methods, high-end computing, and scalable data pipelines. Named entity recognition (NER), part of speech (POS) tagging or sentiment analysis are some of the problems where neural network models have yielded state-of-the-art results. NLP practitioners should focus on major fields where rapid advancements are going on. Some of those fields are like:
Deep Learning Frameworks: There has been remarkable progress in terms of Deep Learning Frameworks for NLP development like Tensorflow, PyTorch, Keras, Caffe, Theano, CNTK, etc. PyTorch is quite popular among academics. Tensorflow is quite popular in the industry, as along with support to develop Neural Networks it also provides scalable data pipelines and production environments.
Deep Learning Architectures: There have been drastic developments in deep learning architectures to beef up NLP and make it more context aware. Recurrent networks, LSTM and GRU kind of architectures have brought disruption in terms of sequential learning. Recent advancements like Capsule networks, Attention networks, etc. have empowered NLP to be more context intelligent.
Language Modellings & Embeddings: Multi-purpose models power NLP applications like machine translation, question answering systems, chatbots, sentiment analysis, etc. Several architectures like ULMFiT, Transformer, BERT, OpenAI’s GPT-2 have given state-of-the-art results in language modeling. Word Embeddings like Word2Vec, Glove, Elmo, FLair, etc. have been useful in providing context-sensitive word representation.
Transfer Learning: The major hurdle for business to develop NLP engines is the availability of data and high computation. But the rise of pre-trained models and transfer learning has made it a reality to allow the learning of a production viable NLP model even from less amount of data. Most of the Embedding models we discussed above is available as pre-trained models. AllenNLP has published pre-trained Elmo embedding for word embedding. A whole collection of pre-trained models is available in Tensorflow-hub starting from BERT to Google’s Universal Sentence Encode.