Text networks, part two: Co-occurrence networks

This post is part of a series of posts about text network analysis. Each post will discuss one approach to algorithmically creating networks from texts. See the primer for an introduction and the list of posts.

Recalling the networks 101, a network in its formal representation consists of nodes, i.e. the basic elements of my data, and edges, i.e. relations between the nodes. When creating a network from a text, one has to answer two basic questions:

  1. What are my nodes?
  2. What constitutes a relation between two nodes?

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How Else to Create Lemmatized Text for Topic Modeling

Here it is at last, the first post in the new blog for the SeNeReKo project. On this blog, we will write about aspects of our work, ongoing experiments and lessons learned. Our work heavily builds on the work of others, and we hope it will be of use beyond our project as well. That is why I want to open this blog with a response to another blog post.

Crowds gather to watch cranes joining two ends of an oil pipeline before the official ceremony commemorating the joining of the pipeline of an oil tanker terminal, Portland, Maine, with refineries in Montreal, Quebec.

In his post How to Create Lemmatized (French) Text for Topic Modeling, Christof described how to make use of the TreeTagger output when preparing texts for Topic Modeling. As a by-product of our own work in SeNeReKo, we aimed for a more generic and hopefully simpler approach to deal with annotations of this kind. We chose WebLicht as a starting point, because it allows to easily build annotation toolchains using independent components. Besides others, it supports TreeTagger for lemmatizing French, so generally WebLicht would be a good match for Christof’s task.

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