NLP enables rapid analysis of huge volumes of text, which is where most of the data driving innovation lives. While some NLP systems still rely on rule-based approaches, the rise of machine learning and other statistical approaches has made NLP significantly more robust over the past decade.
Topic modeling and NLP have the potential to accelerate the front end of innovation across many industries, but remain underutilized. NLP can improve processes including technology landscaping, competitive analysis, and weak signal detection.
When utilized effectively, machine learning can quickly mine data to produce actionable insights, significantly decreasing the time it takes for a comprehensive analysis to be performed. An analysis that would have previously taken weeks can then be reduced to days. There are many relevant applications that use machine learning to leverage speed and comprehensiveness in innovation. Landscaping is used to build a taxonomy that defines the trends for key areas of innovation under a specific topic. Concept similarity can take one piece of content and find other relevant articles, patents, or news to accelerate the innovation process.
The speed conferred through NLP is enabled by the comprehensiveness of topic modeling, which extracts important concepts from text while eliminating the human assumption and bias associated with it. Topic modeling can also be used for competitive portfolio analysis when applied to a corporation instead of a technology, or for weak signal detection when applied to large data sets like news or Twitter.
When defining a successful AI and machine learning strategy, there are a few key points to consider, including whether you’ll buy or build your technology, what data sources you’ll use, and how you’ll leverage experts to define and interpret the data. It’s also important to adapt a culture of acceptance of these tools so that valued human resources see them as an asset to their skills rather than competition.
The confidence and speed AI and machine learning bring to the decision-making process is enabling innovation to happen at a more rapid pace than ever before, but don’t think this means humans are no longer needed. People are still necessary to define the starting points of an analysis, label topics, and extract insights from the data collected.
It’s clear that a collaboration between humans and machines can generate better results, benefiting all involved. If you're interested in learning more about NLP and topic modeling, download our white paper to learn more.
- Whitepaper: Improving Innovation with AI (Free Download)
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- Download the Executive Summary: How to Avoid AI Failures