Document Understanding Applications Powered by Natural Language Processing
Gain Actionable Insights from Your Unstructured Content
Natural language processing (NLP), entity extraction, semantic understanding, and machine learning are increasingly helping enterprises analyze content and extract meaning and knowledge. Document understanding refers to the automatic extraction, classification, and analysis of text documents utilizing these intelligent technologies.
As 80% of all enterprise data is unstructured (text, PDF, reports, images, etc.), document understanding applications are key to helping organizations unlock actionable insights within this massive amount of under-utilized data. For instance, organizations can:
- Examine text documents, such as policies, contracts and legal agreements, financial reports, etc., for variations of risky contract terms and present them to the legal team in order to help identify and reduce risks.
- Use NLP and machine learning to automatically identify and categorize a particular document type (legal, finance, marketing, etc.) so that it can be automatically delivered to the appropriate business function.
- Use NLP and custom machine learning algorithms to analyze the text in CVs and job postings to provide a suggested list of the best candidates. This helps automate the recruiter’s task of sifting through millions of resumes and increase fill rates. Recruiters could also provide feedback into what they thought was the most successful match so that the algorithms can “learn” the patterns and improve future results.
- Build business rules to automatically identify the appropriate action to take with documents stored in expensive on-premises storage (move to lower-cost storage, delete if obsolete, or archive). Machine learning is also incorporated to accurately and quickly detect duplicates/near-duplicates. This allows for storage cost savings as well as a 360-degree view of enterprise data.
BENEFITS OF USING NATURAL LANGUAGE PROCESSING FOR DOCUMENT UNDERSTANDING
- Improve compliance and risk management
- Internal operational efficiencies
- Enhance business processes
COMMON DOCUMENT UNDERSTANDING TECHNIQUES
- Named Entity Recognition
- Sentiment Analysis
- Text Similarity
- Text Classification
- Information Extraction
- Relationship Extraction
- Text Summarization
NATURAL LANGUAGE PROCESSING WORKFLOW FOR EXTRACTING INSIGHTS FROM DOCUMENTS
- Documents can be acquired from multiple sources using secure connectors.
- Content processing technology can help clean, normalize, and enrich text documents to a consistently high standard, enabling search and analytics applications to perform optimally.
- For legacy paper documents, OCR (Optical Character Recognition) can convert different types of documents, such as scanned paper documents, PDF files, or images into editable, sortable, and searchable data.
- NLP and machine learning can then help automatically identify specific pieces of information, such as date, order number, or policy number in digitized documents. The resulted data can then support a multitude of business use cases, including insight discovery and automating processes to improve efficiency.
- Search and analytics capabilities can be integrated to help enterprise users find and analyze information faster and easier.
CONTENT PROCESSING AND NATURAL LANGUAGE PROCESSING TECHNOLOGY
- Content acquisition – acquiring text documents and other types of organizational data from multiple business systems with secure connectors
- Unstructured content processing – Aspire content processing framework for efficient processing of unstructured documents
- Natural Language Understanding (NLU) framework – a scalable, cost-effective, easy-to-use framework that processes and understands complex business documents and user queries. Learn more about Saga NLU framework
- User-friendly search and analytics UI – providing users an enhanced insight discovery experience
Contact us to discuss how document understanding applications can support your business use cases.