How NLP is turbocharging business intelligence

Many of our global customers are deploying our contract review solution to meet these governmental and regulatory obligations. We also have technical challenges that are typical for NLP across industries. Mapping the context, specificity, and personalization of NLP to the industry it serves is challenging. We’ve all seen legal documents with three paragraphs of data that could have been summarized in one sentence. So as we develop NLP for the legal domain, there’s some game theory involved.

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Words possess multiple meanings depending on context, tone, or cultural subtlety. For instance, “bank” can signify a financial institution or the edge of a river. Machines find it challenging to discern subtle distinctions that humans grasp effortlessly. Misinterpretation can lead to communication issues in virtual assistants or chatbots, frustrating users and negatively affecting business interactions. Autonomous AI agents handle complex tasks without constant human oversight. They automate workflows, manage data, and execute decisions based on predefined objectives.

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Accuracy improvements have reached over 90%, even for complex accents or noisy environments. Semantic understanding advances this by interpreting relationships between ideas in the text. AI identifies subtle nuances, like if someone is being sarcastic or expressing concern.

New approaches promise smarter tools for faster, more accurate communication. Machine learning has an opportunity to drastically reduce or remove this burden and allow businesses to refocus on delivering value to their customers. AI for contract review makes it possible to automate the identification of contractual obligations that otherwise would be missed. Enterprises can proactively monitor and fulfill global, regional and local regulatory requirements, where previously this was a reactionary process requiring the payment of large fines when companies were out of compliance. In 2011, Apple introduced Siri, marking a significant milestone as the world’s first NLP/AI assistants. Siri’s innovative system was among the earliest to achieve widespread success.

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Such connections process raw information into practical reports that save time and resources. If users change subjects abruptly, AI may misinterpret intent or provide unrelated responses. This reduces its capability in practical communication settings like chatbots and virtual assistants. Businesses must proceed cautiously when implementing language-processing applications. For example, chatbots and voice assistants may unintentionally store sensitive customer details without adequate protections in place.

• She added that natural language interfaces (NLIs) that are both voice- and text-based can interpret these questions and provide intelligent answers about the data and insights involved.
• Humor, idioms, or polite forms translate poorly without a deeper understanding of local norms.
• Knowledge graphs connect data points, clarifying relationships between them.
• Incorrect interpretations affect sentiment detection or customer feedback analysis for businesses that depend on text tools.
• 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.
• “Natural language querying and natural language explanation are pretty much routinely found in most every BI analytics product today,” Doug Henschen, analyst at Constellation Research, told VentureBeat.

Deep learning algorithms examine sentence structures and cultural details with precision. Many platforms incorporate this feature into virtual assistants and chatbots, simplifying global operations efficiently while reducing expenses on human translators. But she also hopes it will inspire more researchers to look beyond deep learning. The results emphasized to her that true common-sense NLP systems must incorporate other techniques, such as structured knowledge models.

She added that natural language interfaces (NLIs) that are both voice- and text-based can interpret these questions and provide intelligent answers about the data and insights involved. “NLP-driven analytical experiences have democratized how people analyze data and glean insights — without using a sophisticated analytics tool or crafting complex data queries,” added Setlur. When NLP enhancement originally came to BI systems, “it was kind of clunky,” Henschen said. Enterprise developers had to work to curate the language that was common within the domain where the users of the data lived.

“Naive utilization of these approaches may lead to bias and inaccurate summarization. However, there are startups and more established companies creating enterprise versions of these systems to streamline the development of fine-tuned models, which should alleviate some of the current challenges,” said Behzadi. Organizations can automate many workflow tasks through natural language processing to get the relevant data.

Simplified insights save time while providing clarity into how audiences truly feel about products or services. AI also surfaces information that previously would have been difficult to find, because you weren’t looking for it. For example, let’s say a firm is the industry leader in oil and gas law, specializing in mineral rights, and they developed and licensed an AI package to review mineral rights in the state of Wyoming.

Machines have difficulty understanding idioms, sarcasm, or cultural references. For instance, a phrase like “break a leg” might confuse algorithms into interpreting it as physical harm rather than encouragement. Virtual assistants may then respond awkwardly or even inappropriately when addressing customers from varying backgrounds.

According to Yashar Behzadi, CEO and founder of synthetic data platform Synthesis AI, generative AI approaches to NLP are still new, and a limited number of developers understand how to properly build and fine-tune the models. 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. This convenience plays a significant role in promoting an organization’s analytics culture. By applying NLP to BI tools, even non-technical personnel can independently analyze data rather than rely on IT specialists to generate complex reports. As with other technology areas, the field stands to change even more dramatically as large language models like OpenAI’s ChatGPT come online.