Forget the Format Pt 2: Enabling Document Insight Extraction 

As much as 90% of data generated daily is unstructured. 

In part 1 of our series on unstructured content, we focused on the challenges these file types can pose to organizational data management and the collection of business insights. 

Fortunately, new advancements in deep learning models combined with powerful compute infrastructure are making this an issue of the past. But what is it about these models that makes them so accurate at identifying and extracting the most relevant insights from unstructured data sources? And why can’t we just rely on keyword search for this same task?

5 Deep Learning Tasks Advancing Document Insight Extraction

One of the significant benefits today’s deep learning models provide is the ability to search within documents for the most accurate information. Unlike keyword search that is limited to the exact term or phrase used in a query, deep learning models can dive deeper by leveraging semantic terms to identify contextually similar results to a query. 

Some of the major fields of research for deep learning are focused on improving a model’s ability to accurately extract the information a user is interested in. Here are some of the major areas of focus in the field of deep learning text analytics:

• Query Understanding – We’ve all run into the problem of a poorly worded question or a limited boolean query impacting the results of a web search. Query understanding is the process of deep learning text analytics identifying the intent of a query and translating it to the system, eliminating the need for a perfectly worded question to get the best results.
• Named Entity Recognition – Too often query outcomes are limited to the exact keywords in a question. Deep learning enabled named entity recognition enables systems to also identify broader concepts related to a specific search term. For example, a search including the state of “Texas” would also identify other states that may be included in a document. 
• Relationship Extraction – Complementary to named entity recognition, relationship extraction identifies connections between groups of text by using grammar and context. For example, identifying a product and the company that produces said product listed within an unstructured document. 
• Word Embedding – It’s incredibly common for the same topic to be referred to by different terms, but traditional search methods make it challenging to identify each specific word. Deep learning models are being trained with word embedding to identify semantically similar content. For example, when searching for COVID-19, the model will also identify “coronavirus” and “SARS-CoV-2.”   
• Text Ranking – Deep learning models can evaluate results to identify and prioritize the most relevant answers. As a result, less time is spent sifting through redundant or obsolete content.

Saved Time, Improved Accuracy

Applying deep learning-powered text analytics to your unstructured data sources has significant benefits beyond improving data accessibility. Those charged with handling data – whether they’re in IT, R&D or another function – will also save significant time searching for and collecting data while ensuring they’ll have the most accurate insights available. 

In recent benchmarking, Vyasa’s deep learning text analytics improved query accuracy by 97%. With responses delivered in milliseconds, analysis time can be decreased by up to 90%. By reducing the number of hours employees spend on these tasks, businesses can take action quickly,  with the confidence that they have the most accurate insights to deliver positive results.

As the volume of enterprise data continues to grow exponentially, the ability to easily manage, access and glean insights from this content will prove critical to making informed business decisions. 

The Vyasa Layar deep learning data fabric unifies content sources across your environment, regardless of file location or format. Powerful deep learning solutions can be applied to content cataloged within Layar, allowing users to extract insights from documents such as PDFs, presentation decks, published research or images that are rich in data but hard to search via traditional methods. Insights can be accessed via highly visual applications such as dynamic knowledge graphs and tabular data sets, which simplify tasks such as clinical trial discovery and design, rare disease research, and competitor analysis.

Interested in seeing the power of Vyasa in action? Access a 7-day free trial here.

Tanina Cadwell, Solutions Architect at Vyasa, contributed to the content of this blog post.