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Section Overview

9. Section Overview#

This three-part workshop series introduces participants to natural language processing (NLP) with Python. It builds on Getting Started with Textual Data by extending the scope of data-inflected text analysis to include various methods of modeling meaning. Sessions will cover NLP topics ranging from segmentation and dependency parsing to sentiment analysis and context-sensitive modeling. We will also discuss how to implement such methods for tasks like classification. Basic familiarity with analyzing textual data in Python is required. We welcome students, postdocs, faculty, and staff from a variety of research domains, ranging from health informatics to the humanities.

10. Before We Begin…#

This reader is meant to serve both as a roadmap for the overall trajectory of the series and as a reference for later work you may do in natural language processing (NLP). Our sessions will follow its overall logic, but the reader itself offers substantially more details than we may have time to discuss in the sessions. The instructors will call attention to this when appropriate; you are encouraged to consult the reader whenever you would like to learn more about a particular topic.

Session schedule

Session

Chapters Covered

Topic

1

Chapter 2

Document annotation

2

Chapter 3

Text classification and feature engineering

3

Chapter 4

Word embeddings

Learning Objectives

By the end of this series, you will be able to:

  • Use popular NLP frameworks in Python, including Gensim and spaCy

  • Explain key concepts and terminology in NLP, including dependency parsing, named entity recognition, and word embedding

  • Process texts to glean information about sentiment, subject, and style

  • Classify texts on the basis of their features

  • Produce models of word meanings from a corpus

  • Perform a few core NLP tasks including keyword analysis, relation extraction, document similarity analysis, and text summarization

10.1. File and Data Setup#

10.1.1. Google Colab#

We will be using Google Colab’s platform and Google Drive during the series and working with a set of pre-configured notebooks and data. You must have a Google account to work in the Colab environment. Perform the following steps to setup your environment for the course:

  1. Download the data

  2. Un-compress the downloaded .zip file by double clicking on it

  3. Visit the Google Colab website at and sign-in using your Google account

  4. In a separate browser tab (from the one where you are logged-in to Colab) sign-in to your Google Drive

  5. Upload the nlp_workshop_data directory into the root of your Google Drive

Once you have completed the above steps, you will have your basic environment setup. Next, you’ll need to create a blank notebook in Google Colab. To do this, go to Google Colab and choose “File->New Notebook” from the File Menu. Alternatively, select “New Notebook” in the bottom right corner of the notebooks pop-up if it appears in your window.

Now, you need to connect your Google Drive to your Colab environment. To do this, run the following code in the code cell at appears at the top of your blank notebook:

from google.colab import drive
drive.mount('/content/drive')

Your environment should be ready to go!

10.1.2. Template code#

This workshop is hands-on, and you’re encouraged to code alongside the instructors. That said, we’ll also start each session with some template code from the session before. You can find these templates in this start script directory. Simply copy/paste the code from the .txt files into your Jupyter environment.

10.2. Assessment#

If you are taking this workshop to complete a GradPathways micro-credential track, you can find instructions for the assessment here.

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