Natural Language Processing (NLP) research has seen profound changes and new developments in the year 2020. After the modification and introduction of new technologies, NLP requires comparatively lesser quantities of training data than ever before. The introduction of transfer learning and pre-trained language models in Natural Language Processing (NLP) pushed forward language understanding and generation limits.
NLP is the technology that makes it possible for computers to read the texts or hear speech, interpret it, measure sentiment, and extract meaningful information. There are numerous ways to process the human language, and those are the symbolic approach, statistical approach, and connectionist approach. NLP promotes more discussions, result-based tasks, affordable costs, and improved customer interest analysis. To keep up with the updates and latest developments, you can check out the artificial intelligence and machine learning courses online or the AI and ML training course.
Transfer learning and applying transformers to different downstream NLP tasks have become the latest research advances besides positioning and organizing these deep learning models. The conventional rule-based algorithms are developed for more accurate text analytics, conversational AI, and a host of other learning models that explain the exceptional control of this technology. The latest improvements in NLP language models seem to be driven not only by the massive boosts in computing capacity but also by discovering ingenious ways to lighten models while maintaining high performance.
Supervised learning and unsupervised learning collaboration
For supporting natural language processing structurally, the application of both supervised and unsupervised learning is required. In general, natural language processing learning approaches require centralizing the training data on one machine or in a data centre. The combination and working of supervised and unsupervised learning together make this task easier and less time-consuming. One helps them understand all the terms related to the topic, while the other makes establishing the relationship between terms easier.
Training NLP models with reinforcement learning
After making natural language processing stronger, it has become better in terms of efficiency, training period, and performance. Training the RL models from scratch is still comparatively very slow and unstable. The NLP based supervised models are given preference over a scratch model by data scientists to refine the Reinforcement learning.
Accurate Deep learning classification
Deep Learning’s application has many sides where natural language processing is concerned. Data scientists are supposed to do accurate text classification by parsing through applications like Recurrent Neural Networks (RNNs). It is predictable that in text analytics platforms for document classification and entity tagging, RNN can be a convenient trend. With certain improvements, RNN has the potential to categorize documents easily; hence there will be an improved field of the analytics platform.
Market intelligence monitoring
For tracking and monitoring market intelligence reports to extract intelligent information for future strategy formulation, natural language processing is expected to be an important measure. Being successfully implemented in the financial market, NLP might be useful in the business market too. The market sentiments, tender delays, closings, and extractions of large repositories are tracked through NLP.
Fine-tuning models will be seamless
Plans are to make pre-trained models creating applications for sentiment analysis, text classifications, and more. Transfer learning will help the medical authority by recording the patients’ satisfaction with the services provided. Using the same technology, various businesses can derive conclusions based on the likelihood of a satisfied consumer.
Customized product recommendations
The online market is growing progressively every hour. It is also said that this digital platform may replace the traditional market after a point. The marketers involved here can take advantage of NLP to increase customer engagement, analyze their browsing patterns and shopping trends. For the company’s marketing and growth, they can record purchase behaviour, auto-generated product descriptions and more through natural language processing.
Intelligent semantic search
The search engines are retrieving meaningful information intelligently with semantic web technologies. According to Towards Data Science, semantic search is understanding the content provided by the user on any service or product. Thus, it represents “knowledge in a way suitable for meaningful retrieval”. This search uses the functioning of both Natural Language Processing and Natural Language Understanding, requiring a detailed comprehension of the sentiments contained within the text.
Cognitive communication means communication that is easily understandable for both parties. Natural language processing will also be useful for understanding user intent, like intelligent chatbots and semantic search. Aroused by deep learning, natural language processing will continue to mould the communication capacity of cognitive computing. Other deep technical processes behind NLP include machine learning techniques, computational linguistics, and statistics across training corpora.
Growth in chatbots and virtual assistants
With the growth and development of natural language processing, the growth in the chatbot and virtual assistant market would get stronger as a consequence. The chatbot market was worth $2.8 billion in 2019 and is predicted to reach US$142 billion by 2024. However, the implementation of open-domain bots will remain incredibly challenging due to many direct limitations of deep learning.
Sentiment analysis for social media
The implementation of natural language processing can be very fruitful to construct an opinion gathering the feedback and reviews from social media platforms. The comprehension and analysis of the audience responses to a brand communication published on the platforms can be estimated on average to work upon. Open Mining can be undertaken to analyze the feeling of the consumer who is commenting or engaging with the company through social media posts.
1. What are the challenges of natural language processing?
NLP can not sense the irony in the text. The sense of understanding the satire is missing in the NLP.
2. What resources should I use to get started with Natural Language Processing (NLP)?
There are various courses and books for reference. Mostly it is recommended to study the fundamentals first by reading Speech and Language Processing, 2nd Edition by Jurafsky and Martin.
3. What are the benefits of natural language processing?
NLP helps us to perform a large-scale analysis, objective, and accurate analysis. This helps in understanding the quality and demand of customers in business development.