Top 7 NLP trends to look forward to in 2021

Natural language processing, first studied in the 1950s, is one of the most dynamic and exciting areas of artificial intelligence. With the rise of technologies such as chatbots, voice assistants and translators, NLP has continued to show very encouraging developments. In this article, we attempt to predict what the NLP trends will look like in the future by 2021.

Sentiment analysis on social networks

A large amount of data is being generated all the time on social networks. It also creates a particular problem of making sense of all of this generated information, which cannot be done manually.

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NLP has proven to be an important tool in this regard. Used as a sentiment analysis tool, NLP is helping more and more businesses quickly understand what customers think about their brand by analyzing emotion, language, and understanding the urgency of the conversation.

However, NLP still struggles with a confusion deficit, which is the inability to distinguish between the different meanings of a word. It also extends to the contextual meaning of words and phrases and also identifies sarcasm or ironic statements. Another challenge that NLP is currently facing is analyzing statements with multiple meanings, often contradictory.

While it cannot be said with absolute certainty that these issues can be resolved in the near future as 2021, there is room for substantial improvement in NLP techniques in this regard for better emotional and sentimental analysis on issues. social networks.

Multilingual NLP

Most of the progress in NLP has focused on English so far. However, big tech companies like Google and Facebook are now deploying pre-trained multilingual models that perform on par with unilingual models.

Some of the most significant innovations in this direction are Microsoft’s Turing model and Facebook’s M2M-100 model. Turing’s multilingual linguistic model integrates a theoretical information framework for the pre-training of the interlinguistic linguistic model for the understanding and generation of languages ​​to represent 94 languages ​​in the same vector space.

Facebook’s M2M model has been trained in 2,200 languages ​​and can directly translate any pair of 100 languages ​​without using any data into English.

With such innovations and recent advancements in language independent sentence integration and zero shot learning, we can expect to see more innovations in the multilingual NLP model space in the near future.

Chatbots and customer assistants take it to the next level

With advancements in NLP and increasing demands for customer service, one can expect major advancements towards next-generation robots capable of holding complex conversations, improving themselves and learning to perform tasks. for which no training has been provided beforehand.

Due to an increase in remote working situations in 2020, there has also been a huge increase in customer support tickets across all industries. Coping with the increasing volume of tickets and providing rapid responses to urgent requests has become a major task. Integration of NLP tools with technical support software can be expected to perform tasks such as tagging and routing customer support requests, thus requiring human intervention in higher value tasks. added.

Automation in NLP

The success of automated machine learning or autoML in effectively dealing with real-world problems has prompted researchers to develop more automation and code-free tools and platforms.

One of these areas is the automation of natural language processing. With AutoNLP, users can create models such as sentiment analysis with just a few lines of basic code. This encourages wider participation in the machine learning community, previously thought to be restricted to developers and engineers.

AutoNLP helps automate processes such as stemming, tokenization, and lemmatization, among others. It can also help in choosing the best model for a given data set.

We can expect more and more companies to adopt automation and low-code NLP techniques in the near future.

Combination of supervised and unsupervised methods

Separately, NLP models are trained on both supervised and unsupervised methods.

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Supervised learning is commonly used for tasks that require large amounts of labeled data and many iterations for the model to make accurate predictions, such as classifying subjects.

In the case of unsupervised learning, the model learns from the input data and detects patterns to make predictions on invisible data, as in the case of clustering.

Next, NLP models will use a combination of supervised and unsupervised learning to improve performance for tasks such as text analysis.

Deep learning in NLP

In the past, machine learning approaches in NLP problems were largely based on superficial models such as SVM and logistic regression. However, recent trends show a shift towards the use of neural networks to produce superior results on NLP tasks.

Traditional machine learning-based NLP systems are based on features that are designed over time, which is time consuming and often incomplete. Such shortcomings are overcome by models related to deep learning, more specifically convolutional neural networks (CNN), recurrent neural networks (RNN) and recursive neural networks. According to an article titled “Recent Trends in Deep Learning-Based Natural Language Processing”, deep learning techniques optimize NLP models on features such as named entity recognition, semantic tagging of roles, sentiment classification, machine translations, contextual integration and dialogue systems.

Future innovations for NLP deep learning can be expected primarily on two fronts: the distinction between similarity and kinship, and word ambiguity. In the case of the former, current deep learning methods are still based on the degree of correlation rather than the intended meaning, which can result in poor practical applications; whereas word ambiguity refers to the same word but with different meanings.

Market intelligence monitoring

Market awareness and the exchange of information between various organizations, stakeholders, governments and regulators influence commercial markets. Undeniably, it is very important to stay up to date with the rapidly changing trends and standards in the market. In this regard, natural language processing is an important tool for tracking and monitoring market information reports to extract vital information needed for strategy development. NLP is already used in financial marketing, for which it helps determine the market situation, job changes, information related to tenders, extracting information from large repositories, etc.

In 2021, NLP is expected to play a more influential role in the functioning of business organizations. It should make way for the field of market surveillance. Businesses will depend heavily on NLP to plan for future steps. Other applications such as reputation management, neural machine translation, talent acquisition, data visualization and even process automation will have major components of NLP.

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