5 key trends in natural language processing in 2022

Natural Language Processing (NLP) used to be incredibly glitchy. But times are changing and huge improvements have been made.

NLP is a branch of artificial intelligence (AI) that focuses on helping computers understand the way people write and speak. As such, these systems capture the meaning of an input of words and produce an output that can vary depending on the application.

Here are some of the key trends in NLP:

Improved collaboration

Rapid advances in machine learning play a key role in understanding and managing collaboration. You see it in the increasing sophistication of voice prompt systems and in systems that interpret with some accuracy what you need based on a few words. With each passing month, the technology gets better and better.

“While several tools are available that provide a bird’s-eye view of collaboration, innovative applications of NLP and Social Network Analysis (SNA) can provide much deeper insight,” said Jason Morgan, Vice President of Behavioral Intelligence, Conscious

Feeling at the workplace

Modern natural language processing techniques are now able to accurately assess in real time the sentiment, toxicity and current talking points in a workplace. This is probably appreciated by top management, but viewed with suspicion by staff in general. Everything you say via Teams, phone and email can be interpreted and evaluated by faceless software. Obviously, such approaches are open to interpretation. The danger is that a quiet and somewhat antisocial programmer may decide that anything impetuous or enthusiastic should be labeled as counterproductive and turn the workplaces into a morgue. But that’s just one possible outcome.

Workplace spy systems became popular during the early days of the lockdowns. Now that everyone was home, management became suspicious that no one was working. Fortunately, the mind eventually prevailed in most organizations. Such systems are now mainly used to determine general behavior of employees and to identify only the worst offenders. But NLP can take them to another level.

“These tools give leaders unprecedented real-time visibility into an organization’s collaborative environment,” said Morgan. “Tracking organizational sentiment and toxicity can arguably serve as key performance indicators for leaders that support corporate culture, engagement and wellbeing.”

NLP and Business Collaboration

Morgan added that effective use of NLP requires close integration with enterprise collaboration platforms. Only purpose-built tools with deep integrations provide a comprehensive, contextualized view of conversations happening across collaboration platforms.

“It’s important for collaboration leaders to incorporate such tools into their community management workflows to get the most out of their collaboration strategies,” Morgan said.

chatbots

Chatbots are one of the most popular NLP applications today. They have multiplied rapidly in recent years. Why give a human answer when a robot can do it just as well? Or so the theory goes. Some systems are better than others.

“NLP has grown in popularity for its ability to power applications that prove useful to businesses, from customer service chatbots to medical health lines,” said Rachel Roumeliotis, Vice President of AI and Data Content Strategy at O’Reilly Media† “Today, NLP is used to automate a variety of tasks for everyday workers, whether that’s automatically completing a chunk of text or a line of code.

Data analysis

So much of the data we have is either spoken or in text. As time goes by and technology becomes cheaper and faster, NLP could play an important role in collecting and analyzing unstructured data. Roumeliotis believes that NLP will eventually become a de facto technology within organizations.

“In the meantime, with the lack of expertise in AI and the rise of APIs, we will see more AutoML solutions and APIs that you can plug in in the near future,” she said. “They won’t necessarily be state-of-the-art and powerful, but they’ll be good enough for certain use cases, such as chatbots that aren’t mission-critical, especially as we tackle challenges like cost and computing power. To achieve these kinds of mission-critical use cases, such as assistance needed during a medical operation, making models open source will be a critical step in teaching people about NLP and advancing innovations in space at a rapid pace.

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