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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Semaphore Knowledge Modeling with The Accidental Taxonomist
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AI and the Future of Business
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