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Enhanced extraction from Inxight

Inxight has released two new software development kits that offer benefits to its customers: ThingFinder SDK 4.0 and ThingFinder Professional SDK 4.0.

Inxight says ThingFinder SDK provides advanced text analysis technology that facilitates the automatic identification and extraction of key entities from any text data source, in multiple languages. Out of the box, Inxight ThingFinder automatically identifies and extracts more than 35 key entities--the who, what, when and how, such as people, dates, places, companies, e-mail addresses, geo-coordinates, facilities, etc. ThingFinder, says Inxight, enables developers to maximize and extend the value of their applications by enabling users to quickly find the most important pieces of information within large volumes of documents.

The new ThingFinder SDK 4.0 offers major feature enhancements, including a new Java Native Interface and pronoun co-reference resolution, as well as improved processing speed.

ThingFinder Professional 4.0 is claimed to be the most extensible extraction engine available on the market, providing a full suite of capabilities to support user-extensible discovery and extraction of entities, relationships and events in more than 30 languages. A complete linguistic parse is available to create the most accurate patterns possible.

Pre-defined event and relationship extraction packs are available for a variety of uses, including corporate and business intelligence, such as mergers and acquisitions, as well as counterterrorism and law enforcement.

ThingFinder Professional 4.0 SDK delivers new relationship and event rules to get users closer to fact extraction out of the box, including:

• familial and interpersonal relationships,

• people's attributes,

• people's appearance,

• organizational relationships, and

• people and organizations' actions.

Inxight explains that ThingFinder can be integrated into virtually any application that processes textual information, enabling users to create relevant, meaningful structured data from unstructured text to:

• combine the most relevant information in unstructured data with structured data to generate the reports and statistics that drive business;

• automatically find all references to products and people in customer service logs and e-mails and store them in a CRM database;

• discover all company and brand mentions for competitive analysis and business intelligence;

• create link analysis and business intelligence applications that monitor trends and movements associated with people, places, dates and companies;

• mine large volumes of text for relevant information and quickly identify trends in data sets; and

• help classify data in information life cycle management applications.

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