NLP: The Key To Responsible And Practical AI Deployment In Business

Semantic search brings intelligence to search engines, and natural language processing and understanding are important components. That’s why companies often resort to hiring data scientists and data analysts to extract insights from their BI systems. An increasing number of global companies are now adopting NLP-driven business intelligence chatbots that can understand natural language and perform complex tasks related to BI. Question answering is an NLU task that is increasingly implemented into search, especially search engines that expect natural language searches. “Stakeholders and executives can query the data through questions, and their BI platform could respond by providing relevant graphs. NLP and NLU make semantic search more intelligent through tasks like normalization, typo tolerance, and entity recognition.

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Lemmatization will generally not break down words as much as stemming, nor will as many different word forms be considered the same after the operation. This step is necessary because word order does not need to be exactly the same between the query and the document text, except when a searcher wraps the query in quotes. The meanings of words don’t change simply because they are in a title and have their first letter capitalized. Again, normalization generally increases recall and decreases precision.

Intent Detection

In reality, NLP and AI are not two different technologies; NLP is actually a platform to deploy a series of AI capabilities. “Computer systems would need to be able to parse and interpret the many ways people ask questions about data, including domain-specific terms (e.g., the medical industry). Developing robust and reliable tools that can support BI organizations to analyze and glean insights while maintaining security continue to be issues that the field needs to improve upon further,” added Tableau’s Setlur. Organizations can automate many workflow tasks through natural language processing to get the relevant data.

• To date, Mozilla Common Voice’s data set comprises some 1,400 hours of voice samples across 18 languages.
• “With the emergence of LLMs, NLP algorithms can summarize much more accurately and understand the meaning of user-generated content without extracting an endless stream of examples, copied word for word.
• If you want the best possible precision, use neither stemming nor lemmatization.
• “Traditional BI should be complemented by and not replaced with new NLP approaches for the next few years.
• NER will always map an entity to a type, from as generic as “place” or “person,” to as specific as your own facets.
• It is essential to have the support of a specialist in a domain to refine workflow architectures and work together with the data team.

The Role Of Large Language Models

NLP, plus the judicious use of AI, is an important tool for understanding and answering business needs while keeping an eye on the bottom line. NLP isn’t going anywhere and will likely become one of the cornerstones of a company’s AI philosophy and plan. Understanding end users’ preferences and needs is a continuing imperative for NLP and business intelligence, as is the need to programmatically sort through masses of data. “Naive utilization of these approaches may lead to bias and inaccurate summarization. “There are many successful use cases of NLP being used to optimize workflows, and one of them is to analyze social media to identify trends or brand engagement.

“With the emergence of LLMs, NLP algorithms can summarize much more accurately and understand the meaning of user-generated content without extracting an endless stream of examples, copied word for word. That means users can obtain actionable insights through a conversational interface without having to access the BI application every time. Setlur believes this has changed how organizations think of growing their businesses and the types of expertise they hire. “With NLP-enabled chatbots and question-answering interfaces, visual analytical workflows are no longer tied to the traditional dashboard experience.

NLP models can also become more complex, and understanding how they arrive at certain decisions can be difficult. Therefore, it is essential to focus on creating explainable models, i.e., making it easier to understand how the model arrived at a particular decision. Before storing any data, organizations need to consider the user benefits, why the data need to be stored, and act according to regulations and best practices to protect user data,” said Bernardo. One major challenge to implementing NLP in BI is that bias against certain groups or demographics may be found in NLP models. Another is that while NLP systems require vast amounts of data to function, collecting and using this data can raise serious privacy concerns.

Companies can use Rasa’s tools to make their text- and voice-based chatbots perform better — with contextual conversations for applications like sales, marketing, customer service, and more. A user searching for “how to make returns” might trigger the “help” intent, while “red shoes” might trigger the “product” intent. Identifying searcher intent is getting people to the right content at the right time. If you don’t want to go that far, you can simply boost all products that match one of the two values. The best typo tolerance should work across both query and document, which is why edit distance generally works best for retrieving and ranking results.

People can ask questions in Slack to quickly get data insights,” Setlur told VentureBeat. Business intelligence is transforming from reporting the news to predicting and prescribing relevant actions based on real-time data, according to Sarah O’Brien, VP of go-to-market analytics at ServiceNow. As with other technology areas, the field stands to change even more dramatically as large language models like OpenAI’s ChatGPT come online.