Named Entity Recognition using LSTMs with Keras

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Coursera Project Network
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In this Guided Project, you will:

Build and train a bi-directional LSTM with Keras

Solve the Named Entity Recognition (NER) problem with LSTMs

Clock1.5 hours
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 1-hour long project-based course, you will use the Keras API with TensorFlow as its backend to build and train a bidirectional LSTM neural network model to recognize named entities in text data. Named entity recognition models can be used to identify mentions of people, locations, organizations, etc. Named entity recognition is not only a standalone tool for information extraction, but it also an invaluable preprocessing step for many downstream natural language processing applications like machine translation, question answering, and text summarization. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Deep LearningMachine LearningTensorflowLong Short-Term Memory (ISTM)keras

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Project Overview and Import Modules

  2. Load and Explore the NER Dataset

  3. Retrieve Sentences and Corresponding Tags

  4. Define Mappings between Sentences and Tags

  5. Padding Input Sentences and Creating Train/Test Splits

  6. Build and Compile a Bidirectional LSTM Model

  7. Train the Model

  8. Evaluate Named Entity Recognition Model

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step



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