Latest Trends In Natural Language Processing

The last three years have seen Natural Language Processing (NLP) rise into one of the most dominant domains in data science. Aided by discoveries from companies such as Google, NLP has seen an evolution in accuracy, speed and even methodology relied on by computer scientists as they strive to tackle complex problems. 

Today, Natural Language Processing and its manifestations - Natural Language Understanding, Natural Language Generation and Natural Language Interaction – are the most researched fields in Artificial Intelligence. 

NLP has always been incredibly relevant in the world of computer science since it’s the foundation behind text analytics, which in turn supports common use-cases such as semantic search. Now, with the interface between human beings and computers so greatly reduced, it would seem the momentum NLP gained in its early days is not going anywhere soon.

As the year progresses, trends tend to emerge with how the industry adopts and utilizes technology. Further still, other trends stick around from previous years, their initial momentum too great to slow down. Here are some trends expected to dominate the coming year:

1. Increased use of supervised and unsupervised learning

Recent developments have found that machine learning could play a crucial role in the future of Natural Language processing, especially in text analytics. To understand how, one should first recall that NLP is responsible for syntactic and semantic analysis of a piece of text. Once the NLP engine completes its analysis – breaking down different parts of speech, for instance – the machine learning engine can then be introduced to perform more detailed analysis via supervised and unsupervised learning.

Unsupervised learning is responsible for determining the mathematical relationships between the results produced by the NLP engine. Once this is done, supervised learning is applied to these relationship determinations to fine-tune the results with a subset of business rules, addressing the complexity of the findings.

A great example of supervised learning in action would be the deployment of boolean operators and boolean rules. These both play an instrumental role in the creation of data models necessary for carrying out semantic analysis.

2. Increased use in company monitoring

With the continual growth of social media, it’s poised to take over an even more important role in how companies make decisions. For instance, in the wake of a quarterly report, a company can rely on various NLP tools to monitor the sentiments about their company on social media and in the news. Organizations may also benefit from using NLP tools to monitor customer sentiments on various social media channels.

3. Recurrent neural networks are no longer the standard

Recurrent neural networks (RNN) formed the basis of text analysis during the technology’s formative years. This architecture has dominated trends in NLP for years and enabled deep learning to be performed on text using innovations such as Word2vec. It has been the standard relied on by many of the world’s largest corporations. That may not be the case for much longer if developments such as ELMo and BERT are to continue at the pace they have so far achieved.

If you’re a marketer, you may or may not already have noticed the impact of BERT. In late 2019, Google announced that they will be integrating BERT into search results. This way, a new dynamic is added in search engine ranking. Since BERT is really good at reading context from pieces of text, the battle for ranking based on keywords alone may eventually come to a close.

ELMo (Embedding from Language Models) use Recurrent Neural Networks to provide modern embeddings that address many of the shortcomings that traditional approaches suffer from. Multi-layer ELMo architecture allows us to learn a lot more from the context of a piece of text than traditional methods would allow. Lower layers take care of basic grammar and syntactic rules, while upper layers are responsible for extracting contextual semantics.

While RNNs still have wide-spread adoption, it’s probably only a matter of time before they are no longer the leading standard altogether.

4. NLP will find even more new use cases

NLP is expected to dominate human-machine communication for the foreseeable future. The end-goal is to develop an NLP system that enables us to communicate with machines just as easily as we would another person. 

It is expected that the amount of data to be used by NLP systems will grow by a factor of 100 by 2025. The implication is that NLP will play an increasingly important role in our lives, with new use cases emerging year-by-year.

5. The transformer will be a dominant NLP standard 

While ELMo has brought some much-needed change to the world of NLP, such as being able to remember more context for a piece of text, it has one crucial flaw – it has to process input sequentially. A prisoner of its design, ELMo manages this feat by storing the state of all the text but sacrifices the ability to learn longer sequences of text. As such, it takes longer to train.

Ultimately, this also means that it is limited in the sizes of datasets it can train. Considering just how much of a difference the larger datasets make, it’s difficult to overlook this flaw.

The transformer architecture addresses this issue by allowing input to be processed parallel to each other, greatly improving performance. To add to that, it was again improved in 2019 with the release of transformer-XL, which allows for even longer sequences of text to be processed at once. It’s not just a performance boost either.

Since text doesn’t need to be broken up into smaller fragments, more input can be processed at one time, allowing for it to be processed using language’s natural boundaries. For instance, a whole paragraph or sentence can be analyzed, giving it the ability to draw out even more context.

6. More Business Intelligence Uses

As NLP systems grow more capable and efficient, they are going to be more important for organizations looking to collect business intelligence information from raw business data. The implication here is that NLP will help businesses move away from legacy platforms to more modern intelligence-driven platforms.

7. Conclusion

NLP is going to be dominating trends in Artificial Intelligence for the foreseeable future. Some trends within the NLP framework itself, such as the more widespread adoption of pre-trained models, are bound to gain traction among developers. For enterprises, consumer-sentiment monitoring and business intelligence are a few use cases growing in popularity.

Author’s Bio

Edward Huskin is a freelance data and analytics consultant. He specializes in finding the best technical solution for companies to manage their data and produce meaningful insights. You can reach him on his LinkedIn page.