Posted on: March 08, 2019, by: Ann Kelly
Why is Semantic NLP better than standard NLP?
Natural Language Processing is the technology used to assist computer applications in understanding human language. It’s not easy giving machines the ability to understand what humans mean when we communicate, as the rules that dictate how we pass information are complex. It also involves the communication of concepts, something not easy for computers to process as they typically only look at binary strings of characters rather than things.
Some rules are high-level and abstract; for example, when someone uses a sarcastic remark to pass information. Other rules are low-level; for example, using jargon or the character “s” to identify more than one item. To fully comprehend human language, words, as well as context (meaning), are required to understand the intended message. While humans can easily master a language, the ambiguity and imprecise nature of language make NLP difficult for machines to implement.
Taking an NLP-only approach delivers lower quality data output than taking a Semantic + NLP approach. All NLP employs a significant amount of machine learning, which like all learning systems require human governance and control to deliver trustworthy results – an attribute provided by knowledge models rather than difficult to assemble training sets.
Human decision making requires data to be unambiguous, of high quality, and visible – attributes delivered by rules-based classification and semantic enrichment. Extracting facts from unstructured content requires an easy to use fact extraction capability that deals with the vagaries of the textual structure. In this way, Semantics delivers all the benefits of NLP and significantly more.
Using a semantic approach wraps the core NLP engine with additional semantic capabilities:
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Whether you operate in capital markets, banking, or the insurance industry, you need solid information to make decisions that drive positive organization, stakeholder, and customer outcomes. As the volume of data flowing into financial organizations continues to explode, the ability to rapidly access, manage and make sense of it is key to managing risk and improving ROI.
Regardless of sector, financial services organizations are looking for enterprise-grade solutions that allow them to:
The key to managing enterprise data is harmonization, the ability to provide a holistic view of all information, structured and unstructured, regardless of location and type that can be used to manage the business and provide a robust customer experience is key in today’s ever-changing environment.
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Smartlogic’s User Community Forum
Our next User Community Forum meeting will be held on Wednesday, March 13th at 11 am Eastern. If you haven’t signed up yet DO IT TODAY – it’s a great opportunity to meet and connect with like-minded Semaphore users to exchange tips, tricks and best practices with the Semaphore platform and discuss key Knowledge Management topics.
Semaphore Knowledge Modeling with The Accidental Taxonomist
REGISTER TODAY for a live conversation on Thursday, March 28th at 12:00 pm Eastern with Jim Morris, Senior Information Science Consultant at Smartlogic, and Heather Hedden, Senior Vocabulary Editor at Gage and author of The Accidental Taxonomist. You’ll learn how leveraging Smartlogic’s Semantic AI platform Semaphore and implementing Heather’s modeling best practices result in knowledge models that support enterprise initiatives.
AI and the Future of Business
Smartlogic provides training so your team can get the most from the Semaphore platform. Smartlogic will be conducting our full set of Online Semaphore training courses beginning March 4th, 2019. Foundation training is underway but there is still time to register for Advanced and System Administration training.
Semaphore Advanced Training begins April 8th
Semaphore System Administration Training begins May 13th
To learn more about each course, view our events page at https://www.smartlogic.com/events.
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