A cornerstone of customer relationship management, chatbot analytics, and research automation systems, Named Entity Recognition (NER) is a key commercial application of Natural Language Processing (NLP). State of the art approaches to NER are purely data driven, leveraging deep neural networks to identify named entity mentions - such as people, organizations, and locations - in lakes of text data. In this talk, I will present our latest research on NER and provide real-life examples of how we are applying these cutting-edge techniques to ten different languages, including Spanish, English, Arabic, Persian, Korean, and Japanese. We'll look at accuracy, speed, and memory footprint, while comparing some of the best known deep architectures with a basic statistical approach. I will focus on the interpretation of the network, when assigned to learn names
across many languages.
We’ll start with a detailed description of our neural architecture for NER, which is based on a generic Long Short-Term Memory (LSTM) implementation, a specific flavor of recurrent neural network for sequence tagging. We encode word as well as letter embeddings as a single neural pipeline. Our decoder is based on Conditional Random Fields (CRF), leveraging label distributions from across the entire input text. We will then look into the internal network activation values, on different input conditions, with a special focus on highly inflected languages. Our latest findings show key neurons that get activated for different linguistic aspects.
Speaker: Kfir Bar, Basis Technology
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