Text Extraction Technology
Gain knowledge for better decision making
Fact extraction is one of the most frequently encountered tasks in document analysis. The goal is to recognize and extract various relationships between entities of interest, as well as vectors of attributes characterizing these entities and relationships. The extracted facts are organized in a knowledge graph facilitating simple searching, analysis and graphical presentation of important information. Manual extraction of facts from thousands of lengthy documents represents a highly labor-intensive task. At the same time, automated fact extraction represents one of the most difficult challenges in text analytics.
Machine Learning for Documents
Megaputer offers a flexible fact extraction solution that can be quickly customized for any specific application domain. The solution capitalizes on advanced linguistic and semantic analysis performed by PolyAnalyst™, extended by powerful pattern recognition capabilities. It extracts facts of interest and organizes them in ontologies that can be used for further analysis and information retrieval.
A properly customized fact extraction solution can solve numerous business tasks in diverse fields.
Competitive Intelligence
An investment management company wants to monitor media publications to collect information about an emerging technology, identifying key players in this market, their products, business relationships between manufacturers, distributors, suppliers, and investors.
Monitoring Investments and Partners
A large financial institution wishes to monitor media publications for early detection of negative signals affecting their key customers and partners. These signals might range from bankruptcy, corporate conflict, and change of ownership, to industry-specific facts such as the oil price shift or an outbreak of a disease.
Legal document analysis
When your company is dealing with thousands of custom agreements with different customers, it becomes difficult to manage and timely enforce all terms contained therein. Is it possible to extract all relevant terms and organize them in the structured form that facilitates better business processes?
Adverse Event Analysis
Pharma and Life Science companies need to analyze millions of potential adverse event reports they receive from patients and doctors all over the globe. Their goal is to detect and classify into a large standard ontology true adverse events, excluding irrelevant information such as indications, patient and family history.
Research Literature Analysis
To stay on top of current developments, researchers need to perform focused analysis of piles of academic publications. For example, in the pharma or medical domain they might need to track relationships between different diseases, biomarkers, and expressions of various genes, extracting these facts from research articles available through PubMed, which contains references to over 25 million papers.
Insurance Claims Analysis
An insurance company needs to extract key information about an incident described in claim notes that were entered by multiple people with different purposes, and at different moments in time. Extracting and differentiating information about actions performed by the insured versus the claimant is not an easy task.
Fact Extraction from Medical Records
The effectiveness of selecting the proper medical treatment critically depends on how quickly and accurately one can extract relevant clinical information about the patient. Extracting important clinical facts from medical records and organizing them in actionable format becomes possible through the use of specialized text analysis systems.
Benefits
