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Semantic similarity measuring using recursive auto-encoders.

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#7 PyData Warsaw

Bartosz Biskupski & Wojciech Walczak (Samsung) - How much meaning can you pack into a real-valued vector? Semantic similarity measuring using recursive auto-encoders.

The presentation will start with a brief overview of AI research and development at Samsung R&D in Poland. We will then describe a solution, developed in one of our projects, that has won the Semantic Textual Similarity (STS) task within the SemEval 2016 research competition. The goal of this competition was to measure semantic similarity between two given sentences on a scale from 0 to 5. At the same time the solution should replicate human language understanding. The presented model is a novel hybrid of recursive auto-encoders (a deep learning technique) and a WordNet award-penalty system, enriched with a number of other similarity models and features used as input for Linear Support Vector Regression.

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