NetOwl

NetOwl is a suite of multilingual text and identity analytics products that analyze big data in the form of text data – reports, web, social media, etc. – as well as structured entity data about people, organizations, places, and things.

NetOwl utilizes artificial intelligence (AI)-based approaches, including natural language processing (NLP), machine learning (ML), and computational linguistics, to extract entities, relationships, and events; to perform sentiment analysis; to assign latitude/longitude to geographical references in text; to translate names written in foreign languages; and to perform name matching and entity resolution."SRA International." Washington Post. Retrieved 2013-07-02.Zelenko, Dmitry, and Chinatsu Aone. “Discriminative Methods for Transliteration.” In Proceedings of 2006 Conference Empirical Applications of Natural Language Processing (2006). Retrieved 2013-05-20.Maybury, Mark (2012). Multimedia Information Extraction, Hoboken, New Jersey: John Wiley & Sons, Inc., p. 18. Retrieved 2013-07-02. NetOwl's uses include semantic search and discovery,Jackson, Peter; Moulinier, Isabelle (2002). Natural Language Processing for Online Applications: Text Retrieval, Extraction, and Categorization, Philadelphia: John Benjamins B.V., p. 117, . Retrieved 2013-07-02. geospatial analysis,Smith, Susan. “Notes from the GEOINT 2007 Symposium.” GISCafe (2007-10-29). Retrieved 2013-07-02. intelligence analysis,Indurkhya, Nitin et al. (2005). Text Mining: Predictive Methods for Analyzing Unstructured Information, New York: Springer, p. 154-155, . Retrieved 2013-07-02. content enrichment,Guess, Angela (2012-01-19). "LexisNexis Releases New Version of Lexis Advance". semanticweb.com. Retrieved 2013-07-28. compliance monitoring,Aone, Chinatsu, et al. “Assentor®: an NLP-based Solution to E-mail Monitoring.” In Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence (2000), pp. 945-540. Retrieved 2013-05-20. cyber threat monitoring,"SRA’s Dusty Rhoads and Jim McClave: How to strengthen your company’s cybersecurity". ExecutiveBiz (2009-07-02). Retrieved 2013-08-06. risk management,“Nasdaq on the prowl.” CNN Money (1997-09-17). Retrieved 2013-07-02. and bioinformatics.Zaremba, Sam, et al. “Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens.” BMC Bioinformatics, 10:177 (2009-06-10). Retrieved 2013-05-20.

History

The first NetOwl product was NetOwl Extractor, which was initially released in 1996.Hudgins-Bonafield, Christine. “Filtering Knowledge On The Net Just Got Simpler.” NetWork Computing (1996-05-31). Retrieved 2013-09-03. Since then, Extractor has added many new capabilities, including relationship and event extraction, categorization, name translation, geotagging, and sentiment analysis, as well as entity extraction in other languages. Other products were added later to the NetOwl suite, namely NameMatcher and EntityMatcher.

NetOwl has participated in several 3rd party-sponsored text and entity analytics software benchmarking events. NetOwl Extractor was the top-scoring named entity extraction system at the DARPA-sponsored Message Understanding Conference MUC-6 and the top-scoring link and event extraction system in MUC-7.Sundheim, Beth M. "Overview of Results of the MUC-6 Evaluation." In Proceedings of the 6th Conference on Message Understanding (1995), pp. 13-31. Retrieved 2013-05-20.Aone, Chinatsu, et al. "SRA: Description Of The IE2 System Used For MUC-7." In Proceedings of the 7th Message Understanding Conference (1998). Retrieved 2013-05-20. It was also the top-scoring system at several of the NIST-sponsored Automatic Content Extraction (ACE) evaluation tasks.The ACE 2005 (ACE'05) Evaluation Plan. Retrieved 2013-05-20. NetOwl NameMatcher was the top-scoring system at the MITRE Challenge for Multicultural Person Name Matching.

Products

The NetOwl suite includes, among others, the following text and entity analytics products:

Text Analytics

NetOwl Extractor performs entity extraction from unstructured texts using natural language processing (NLP), machine learning (ML), and computational linguistics. Extractor also performs semantic relationship and event extraction as well as geotagging of text. It is used for a variety of data sources including both traditional sources (e.g., news, reports, web pages, email) and social media (e.g., Twitter, Facebook, chats, blogs). It has been integrated with a number of 3rd party analytical tools such as Esri ArcGIS.

Identity Analytics

NetOwl NameMatcher and EntityMatcher perform name matching and entity resolution for large multicultural and multilingual entity databases using machine learning (ML) and computational linguistics approaches. They are used for applications such as anti–money laundering (AML), watch lists, regulatory compliance, fraud detection, etc.

NetOwl

Bron en licentie