Applying the four step "Embed, Encode, Attend, Predict" framework to predict document similarity

Thanks! Share it with your friends!

You disliked this video. Thanks for the feedback!

Added by admin
23 Views
Description
This presentation will demonstrate Matthew Honnibal's four-step "Embed, Encode, Attend, Predict" framework to build Deep Neural Networks to do document classification and predict similarity between document and sentence pairs using the Keras Deep Learning Library.

Abstract
A new framework for building Natural Language Processing (NLP) models in the Deep Learning era has been proposed by Matthew Honnibal (creator of the SpaCy NLP toolkit). It is composed of the following four steps - Embed, Encode, Attend and Predict. Embed converts incoming text into dense word vectors that encode its meaning as well as its context; Encode adapts the vector to the target task; Attend forces the network to focus on the most important parts of the data; and Predict produces the network's output representation. Word Embeddings have revolutionized many NLP tasks, and today it is the most effective way of representing text as vectors. Combined with the other three steps, this framework provides a principled way to make predictions starting from unstructured text data. This presentation will demonstrate the use of this four step framework to build Deep Neural Networks that do document classification and predict similarity between sentence and document pairs, using the Keras Deep Learning Library for Python.

www.pydata.org

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Category
PyData Seatle 2017

Post your comment