Dmitry Сhaplinsky


Senior developer/tech lead @ Zone Digital

Dmitry started coding with pen and paper at the age of 11 and later won several CAD/CAM/CAE contests. He’s passionate about artificial intelligence, natural language processing, neural networks, Arduino Robotics, JavaScript and Python. For the last 1.5 years, he has been a part of Ukrainian NGO White Collar Hundred, building a number of successful IT projects in the field of transparency and anticorruption with the help of open data and natural language processing.

Topic: Building decent NER for Ukrainian language


Named-entity recognition (NER) (also known as entity identification, entity chunking and entity extraction) is a subtask of information extraction. It seeks to locate and classify named entities in text into pre-defined categories such as the names of persons, organizations, locations, etc.

While state-of-the-art NER systems for English are producing near-human performance for languages like Ukrainian, it’s still impossible to do that in an automatic fashion, so we decided to fix it.

We’ve manually annotated corpus with named entities and published it in the open domain. We also trained two models on this corpus: MITIE and deep learning neural network. In my talk, I’ll describe the architecture of the latter one and show how it’s now possible to build a high performant neural network for NLP using only word embeddings as the feature.

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