What is Natural Language Processing?

  

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One of the greatest challenges facing innovation and knowledge workers – those folks in your organization tasked with developing new products, finding new markets, and improving existing products – is accessing the information they need in order to solve problems and make decisions.

But accessing knowledge – even internal corporate knowledge – is a time-consuming, tedious and often fruitless undertaking, with some studies claiming knowledge workers spend 30 percent or more of their time performing the ubiquitous task of ‘searching’.

Traditional Search is a Failed Strategy

A wealth of corporate and common knowledge lies in unstructured text documents such as corporate knowledge and email repositories and, externally, in patent databases, disclosures, technical reports and abstracts, Big Data, social media and more.  The explosion in these unstructured text documents has presented a formidable challenge in computer-aided knowledge extraction to professionals in all fields.  And simple keyword searches, advanced Boolean searches and statistically-based methods, have long been inadequate - retrieving piles of seemingly disconnected documents, rather than the precise answers product development teams seek.

For innovation and technical problem solving, in particular, these conventional search technologies are a failed strategy because the process of matching keywords cannot understand the context of the user’s request – their design intent and need. 

Natural Language Processing: Glean Insights from Unstructured Documents

Effective knowledge retrieval demands that computers are able to correctly analyze the information requirements of the user, and to precisely match these requirements to the contents of the documents being searched.  To accomplish this level of intelligence in automation, the natural language text of the source documents and query must be analyzed into elements that convey meaning, and then employ these methods as the basis for unambiguous comparison between the query and the documents.

Semantic search – particularly Natural Language Processing (NLP) and question answering technology – promises to help make the finding of information via queries easier, faster and more effective.

Powered by a world class semantic search technology, Invention Machine’s Goldfire software, transforms unstructured documents into an index that, when searched, delivers precise and relevant results.   Goldfire’s Natural Language query interface enables the user to submit a question in a free text format, which would be the same format as if the question were given to another person. And, once relevant knowledge has been retrieved, Goldfire presents the results in a way that makes their meaning readily apparent. 

Goldfire integrates proven ideation and problem solving tools and methods with state-of-the-art semantic technology enabling users to leverage relevant concepts from corporate and external content. This marriage of high-level concept extraction and knowledge-enabled problem-solving capabilities ensures engineers, scientists, researchers and other innovation workers are better informed and more creative and comprehensive in their thinking.