nlp

A novel solution for a data augmentation and bias problem in NLP using TensorFlow

A novel solution for a data augmentation and bias problem in NLP using TensorFlow

February 24, 2020

Join KC Tung to discover a way to use TensorFlow to solve a natural language processing (NLP) model bias problem with data augmentation for an enterprise customer (one of the largest airlines in the world). KC leveraged hidden gems in tf.data and the new API to easily find a novel use for text generation and found it surprisingly improved his NLP model.
Deep Learning Toolkit for Splunk

Deep Learning Toolkit for Splunk

February 24, 2020

The Deep Learning Toolkit for Splunk allows you to integrate advanced custom machine learning systems with the Splunk platform using TensorFlow 2.0, PyTorch and NLP libraries. Jupyter Lab Notebooks are providing data scientists and machine learning developers with an integrated experience from rapid prototyping to operationalising models in production. The app is freely available on splunkbase.
Natural language processing using transformer architectures

Natural language processing using transformer architectures

February 23, 2020

Transformer architectures have taken the field of natural language processing (NLP) by storm and pushed recurrent neural networks to the sidelines. Aurlien Gron examines transformers and the amazing language models based on them (e.g., BERT and GPT 2) and shows how you can use them in your projects.
Make Alexa and Siri speak with each other: Toward a universal grammar in AI

Make Alexa and Siri speak with each other: Toward a universal grammar in AI

February 18, 2020

More than 50% of all interactions between humans and machines are expected to be speech-based by 2022. The challenge: Every AI interprets human language slightly different. Tobias Martens details current issues in NLP interoperability and uses Chomsky's theory of universal hard-wired grammar to outline a framework to make the human voice in AI universal, accountable, and computable.
NLP for healthcare: Feature engineering and model diagnostics

NLP for healthcare: Feature engineering and model diagnostics

February 18, 2020

Natural language processing (NLP) is hard, especially for clinical text. Manas Ranjan Kar explains the multiple challenges of NLP for clinical text and why it's so important that we invest a fair amount of time on domain-specific feature engineering. Its also crucial to understand to diagnose an NLP model performance and identify possible gaps.
Natural language understanding at scale with Spark NLP

Natural language understanding at scale with Spark NLP

February 11, 2020

David Talby, Alex Thomas, Saif Addin Ellafi, and Claudiu Branzan walk you through state-of-the-art natural language processing (NLP) using the highly performant, highly scalable open source Spark NLP library. You'll spend about half your time coding as you work through four sections, each with an end-to-end working codebase that you can change and improve.
Protecting the healthcare enterprise from PHI breaches using streaming and NLP

Protecting the healthcare enterprise from PHI breaches using streaming and NLP

February 11, 2020

Hospitals small and large are adopting cloud technologies, and many are in hybrid environments. These distributed environments pose challenges, none of which are more critical than the protection of protected health information (PHI). Jeff Zemerick explores how open source technologies can be used to identify and remove PHI from streaming text in an enterprise healthcare environment.
Can behavioral analytics for enterprise security benefit from approaches in NLP?

Can behavioral analytics for enterprise security benefit from approaches in NLP?

February 7, 2020

While network protocols are the language of the conversations among devices in a network, these conversations are hardly ever labeled. Advances in capturing semantics present an opportunity for capturing access semantics to model user behavior. Ram Janakiraman explains how, with strong embeddings as a foundation, behavioral use cases can be mapped to NLP models of choice.
Named entity recognition at scale with deep learning

Named entity recognition at scale with deep learning

February 4, 2020

Twitter is whats happening in the world right now. To connect users with the best content, Twitter needs to build a deep understanding of its noisy and temporal text content. Sijun He and Ali Mollahosseini explore the named entity recognition (NER) system at Twitter and the challenges Twitter faces to build and scale a large-scale deep learning system to annotate 500 million tweets per day.
Putting cutting-edge modern NLP into practice

Putting cutting-edge modern NLP into practice

February 3, 2020

AllenNLP is a PyTorch-based library designed to make it easy to do high-quality research in natural language processing (NLP). Joel Grus explains what modern neural NLP looks like; you'll get your hands dirty training some models, writing some code, and learning how you can apply these techniques to your own datasets and problems.
Ubers deep learning applications in NLP and conversational AI

Ubers deep learning applications in NLP and conversational AI

February 2, 2020

Uber applies natural language processing (NLP) and conversational AI in a number of business domains. Huaixiu Zheng details how Uber applies deep learning in the domain of NLP and conversational AI. You'll learn how Uber implements AI solutions in a real-world environment, as well as cutting-edge research in end-to-end dialogue systems.
A hands-on introduction to natural language processing in Python

A hands-on introduction to natural language processing in Python

February 1, 2020

With the advent of voice-based assistants and chatbots in our homes, our phones, and our computers, businesses, stakeholders, and developers want to learn about language processing. Grishma Jena introduces you to natural language processing (NLP) using Python. You'll start off with textual data and learn how to process it to derive useful insights that can be used in real-world applications.
Dealing with data scarcity in natural language processing

Dealing with data scarcity in natural language processing

January 13, 2020

In this age of big data, NLP professionals are all too often faced with a lack of data: written language is abundant, but labeled text is much harder to come by. Yves Peirsman outlines the most effective ways of addressing this challenge, from the semiautomatic construction of labeled training data to transfer learning approaches that reduce the need for labeled training examples.
Spark NLP in action: How Indeed applies NLP to standardize rsum content at scale

Spark NLP in action: How Indeed applies NLP to standardize rsum content at scale

January 6, 2020

Alexander Thomas and Alexis Yelton demonstrate how to use Spark NLP and Apache Spark to standardize semistructured text, illustrated by Indeed's standardization process for rsum content.
The unreasonable effectiveness of transfer learning on NLP

The unreasonable effectiveness of transfer learning on NLP

January 5, 2020

Transfer learning has been proven to be a tremendous success in computer visiona result of the ImageNet competition. In the past few months, there have been several breakthroughs in natural language processing with transfer learning, namely ELMo, OpenAI Transformer, and ULMFit. David Low demonstrates how to use transfer learning on an NLP application with SOTA accuracy.
Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills

Beyond Word2Vec: Using embeddings to chart out the ebb and flow of tech skills

January 2, 2020

Word embeddings such as word2vec have revolutionized language modeling. Maryam Jahanshahi discusses exponential family embeddings, which apply probabilistic embedding models to other data types. Join in to learn how TapRecruit implemented a dynamic embedding model to understand how tech skill sets have changed over three years.
Chargrid: Understanding 2D documents

Chargrid: Understanding 2D documents

January 1, 2020

Anoop Katti explores the shortcomings of the existing techniques for understanding 2D documents and offers an overview of the Character Grid (Chargrid), a new processing pipeline pioneered by data scientists at SAP.
AutoML and interpretability in healthcare

AutoML and interpretability in healthcare

December 26, 2019

The healthcare industry requires accuracy and highly interpretable models, but the data is usually plagued by missing information and incorrect values. Enter AutoML and auto-model interpretability. Taposh DuttaRoy and Sabrina Dahlgren discuss tools and strategies for AutoML and interpretability and explain how KP uses them to improve time to develop and deploy highly interpretable models.
NLP from scratch: Solving the cold start problem for natural language processing

NLP from scratch: Solving the cold start problem for natural language processing

December 22, 2019

How do you train a machine learning model with no training data? Michael Johnson and Norris Heintzelman share their journey implementing multiple solutions to bootstrapping training data in the NLP domain, covering topics including weak supervision, building an active learning framework, and annotation adjudication for named-entity recognition.
Introducing KFServing: Serverless Model Serving on Kubernetes

Introducing KFServing: Serverless Model Serving on Kubernetes

December 17, 2019

Production-grade serving of ML models is a challenging task for data scientists. In this talk, we'll discuss how KFServing powers some real-world examples of inference in production at Bloomberg, whic …