Word2complex

Word2complex

A workshop with Varia

This workshop was a play on word2vec, a model commonly used to create ‘word embeddings’. Word embeddings is a technique used to prepare texts for machine learning. After splititng the writing up in individual words, Word2vec assigns a number to each individual word based on its nearness to other words they find themselves in. With word2complex Varia proposed a thought experiment to resist the flattening of meaning that is inherent to such a method, trying to think about ways to keep complexity in machinic readings of text material.

Step 1: Cutting embeddings of words

Choose a body of texts that you would like to analyse. Count how many times words appear in this text. You can use a script or an on-line service. Pick one word from the list of words.

Step 2: Embedding words

Use CTRL+F to find your word in the text that you are analysing. For each moment in which the word is used: describe briefly the context in which the word is.

Examples

Word: street
Embedding:

• "We've been talking to people more involved in both intellectual and academic work on, for example, like Nadia on solidarity and Islamophobia, on thinking about colonial structures in organizing and activism on the street." > activism
• "From Brussels, the food collection was allowed so I think in the streets you had long lines queueing up of people. They managed to do it in a way that was respectful of the social distancing measures basically." > survival

Word: Companies
Embedding:

• "I think a magnitude level failure with our tax money, obviously, that went to their friends, but the collaboration around formulating and writing about extractivism, colonialism, settler colonialism, capitalism and how that manifests itself in moments of crisis like COVID, and the Shock Doctrine approach of companies and governments to implement things like track and trace and now probably also, what's it called certificate, of vaccine certificate." > crisis
• "Then there was an ongoing boycott by left-wing people of the companies that closed their doors to protest this." > refusal

Step 3: Identify/generate/complexify relations

Pick two words that have been embedded (this can include words that someone else embedded. Expand the semantic map below and feel free to adjust the connectors (they are starting points, not prompts)!

Examples

biography is to resources as relation is to available

shitstorm -> war on terror - "In the meantime, I got sidetracked by the shitstorm that has been happening around us for the last 10 years."

violence is to policies as shitstorm is to war on terror
violence is to a fascist street as shitstorm is to war on terror
shitstorm is to war on terror not as the state is to context

Semantic map

______ is to ______ as ______ is to ______
______ is to ______ not as ______ is to ______
______ is not to ______ as ______ is to ______
______ is to ______ as ______ is not to ______
______ ..... ______ ..... ______ ..... ______