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Airbnb: Driving a Higher Level of Customer Support with Machine Learning

At Airbnb, Machine Learning (ML) and Natural Language Processing (NLP) hold the promise to facilitate, optimize and improve customer experiences. Such applications range from understanding customer feedbacks and issues to providing a more reachable and efficient service in order to resolve the issues more effectively. In this talk, Yashar Mehdad, a Data Scientist Manager at Airbnbwill highlights various ways in which ML and NLP techniques are used in supporting their customers.


Natural Language Understanding @ Facebook Scale

At Facebook, text understanding is the key to surfacing content that’s relevant and personalized, plus enabling new experiences like social recommendations and Marketplace suggestions. In this talk, Rushin Shah, Engineering Leader, at FacebookI introduces to DeepText, Facebook’s platform for text understanding, and discuss the various models it supports.


Relationship Extraction from Unstructured Text-Based on Stanford NLP with Spark

Learn how Capgemini developed an automated solution based on Spark integration of Stanford NLP that processes the semantic structure of the sentences, retrieves pieces of supply chain information, matches those to the pieces of the supply chain coming from other sentences in other reports and, finally, presents it to the final user in a form of a graph. The benefits of Spark implementation allowed to treat entire collection of the reports in memory, easily integrate external Stanford NLP libraries.


From Single-Tenant Hadoop to 3000 Tenants in Apache Spark: Experiences from Watson Analytics

How IBM Watson Analytics for Social Media uses Apache Spark for deep text analytics and predictive analytics.


How We Built an Event-Time Merge of Two Kafka-Streams with Spark Streaming

Ralf and Sebastian from Otto GmbH & Co KG explain how they handle data tracking of using Kafka streams. They further discuss how they developed a Spark microservice which utilizes a custom DStream which merges two streams.