Sprylogics Technology Update

About this update from Bragg Gaming Group Inc.

TORONTO, April 10, 2012 /CNW/ - Sprylogics International Corp. - TSX Venture: SPY   ("Sprylogics" or the "Company") is pleased to provide a technology update: The Sprylogics Technology Team is focused on leveraging and enhancing the existing patent pending technology base to build mobile search solutions that interpret what people are saying online and on their mobile phones in order to:  a) Better understand what they are looking for (query intent);  b) Better understand trends and patterns in people's behavior and opinions in aggregate (improve quality and relevancy of search results). This is accomplished through the use of semantic technologies and natural language processing techniques like entity extraction, semantic graph creation, disambiguation, matching and clustering to process massive volumes of unstructured data in order to extract key sentiments, facts, opinions, user interests and intents. The team which is comprised of multiple engineers, data scientists, semantic researchers and mobile application developers and includes 3 PhDs is focused on four streams of development activities.  1. Named Entity, Fact, and Opinion Extraction: The Sprylogics Natural Language Processing team has created an Software Development Kit ("SDK") that enables partners to extract entities, facts and opinions out of unstructured text. The SDK uses a combination of statistical techniques (machine learning trained algorithms) and formal grammar to analyze sentence structure, identify participating entities and extract relationships between sentence elements that signify facts and opinions.  The extraction team has recently expanded the core Natural Language Processing capabilities of the platform through improved precision and accuracy of entity, fact and opinion extraction and has recently distributed an updated version of the Natural Language extraction SDK to select partners. 2. Machine Learning: The Sprylogics Machine Learning team has evaluated and implemented various machine learning algorithms for the purposes of text classification. Trained algorithms are able to classify sentences in natural format as they occur in common conversations, such as in chats, on Facebook or natural language search queries into multiple verticals which enable querying the most appropriate APIs for obtaining answers. The algorithms developed ...

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