Word2complex - Revision history

Varia at 21:33, 14 November 2021

2021-11-14T21:33:07Z

← Older revision		Revision as of 21:33, 14 November 2021
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	== Word2complex ==		== Word2complex ==

	'''A workshop with Varia'''		'''A workshop with Varia (Cristina Cochior and Manetta Berends)'''

	This workshop was a play on [https://en.wikipedia.org/wiki/Word2vec 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 list of number to each individual word based on what other words they find themselves in the company of. Once trained, such a model deducts synonymous words from comparing contexts, or will suggest probable words to complete partial ~~sentence~~. With word2complex Varia proposed a thought experiment to resist the flattening of meaning that is inherent in such a method, trying to think about ways to keep complexity in machinic readings of situated text materials.		This workshop was a play on [https://en.wikipedia.org/wiki/Word2vec word2vec], a model commonly used to create ‘word embeddings’. Word embeddings is a technique used to prepare texts for machine learning. After splitting the writing up in individual words, word2vec assigns a list of number to each individual word based on what other words they find themselves in the company of. Once trained, such a model deducts synonymous words from comparing contexts, or will suggest probable words to complete partial sentences. With ''word2complex'' Varia proposed a thought experiment to resist the flattening of meaning that is inherent in such a method, trying to think about ways to keep complexity in machinic readings of situated text materials.

	[[File:Word2complex.png\|600px]]		[[File:Word2complex.png\|600px]]
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	=== Step 3: Identify/generate/complexify relations ===		=== 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)!		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)!

	==== Example ====		==== Example ====

MMo at 16:45, 20 October 2021

2021-10-20T16:45:21Z

← Older revision		Revision as of 16:45, 20 October 2021
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	==== Example ====		==== Example ====
	<~~pre~~>		<div class="nobreak">
	shitstorm -> war on terror		shitstorm -> war on terror
	- "In the meantime, I got sidetracked by the shitstorm that has been happening around us for the last 10 years."		- "In the meantime, I got sidetracked by the shitstorm that has been happening around us for the last 10 years."
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	violence '''is to''' a fascist street '''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		shitstorm '''is to''' war on terror '''not as''' the state '''is to''' context
	</~~pre~~>		</div>
	=== Semantic map ===		=== Semantic map ===
	<~~pre~~>		<div class="nobreak">
	______ is to ______ as ______ is to ______		______ is to ______ as ______ is to ______
	______ is to ______ not as ______ is to ______		______ is to ______ not as ______ is to ______
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	______ is to ______ as ______ is not to ______		______ is to ______ as ______ is not to ______
	______ ..... ______ ..... ______ ..... ______		______ ..... ______ ..... ______ ..... ______
	</~~pre~~>		</div>

MMo at 16:43, 20 October 2021

2021-10-20T16:43:07Z

← Older revision		Revision as of 16:43, 20 October 2021
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	==== Example ====		==== Example ====
			<pre>
	shitstorm -> war on terror		shitstorm -> war on terror
	- "In the meantime, I got sidetracked by the shitstorm that has been happening around us for the last 10 years."		- "In the meantime, I got sidetracked by the shitstorm that has been happening around us for the last 10 years."
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	violence '''is to''' a fascist street '''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		shitstorm '''is to''' war on terror '''not as''' the state '''is to''' context
			</pre>
	=== Semantic map ===		=== Semantic map ===
			<pre>
	______ is to ______ as ______ is to ______		______ is to ______ as ______ is to ______
	______ is to ______ not as ______ is to ______		______ is to ______ not as ______ is to ______
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	______ is to ______ as ______ is not to ______		______ is to ______ as ______ is not to ______
	______ ..... ______ ..... ______ ..... ______		______ ..... ______ ..... ______ ..... ______
			</pre>

FS: /* Step 1: Cutting embeddings of words */

2021-10-15T08:54:10Z

Step 1: Cutting embeddings of words

← Older revision		Revision as of 08:54, 15 October 2021
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	Choose a body of texts that you would like to analyse.		Choose a body of texts that you would like to analyse.
	Count how many times words appear in this text. You can use a custom script or an [https://databasic.io/en/wordcounter/ on-line service].		Count how many times words appear in this text. You can use a custom script or an [https://databasic.io/en/wordcounter/ on-line service].
	Pick one word from the list ~~of words~~.		Pick one word that appears at least twice from the list.

	=== Step 2: Embedding words ===		=== Step 2: Embedding words ===

FS: /* Step 1: Cutting embeddings of words */

2021-10-15T08:53:35Z

Step 1: Cutting embeddings of words

← Older revision		Revision as of 08:53, 15 October 2021
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	=== Step 1: Cutting embeddings of words ===		=== Step 1: Cutting embeddings of words ===
	Choose a body of texts that you would like to analyse.		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.		Count how many times words appear in this text. You can use a custom script or an [https://databasic.io/en/wordcounter/ on-line service].
	Pick one word from the list of words.		Pick one word from the list of words.

FS: /* Examples */

2021-10-14T11:17:55Z

Examples

← Older revision		Revision as of 11:17, 14 October 2021
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	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)!		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~~ ====		==== Example ====

	~~biography '''is to''' resources '''as''' relation '''is to''' available~~

	shitstorm -> war on terror		shitstorm -> war on terror

FS: /* Examples */

2021-10-14T11:17:19Z

Examples

← Older revision		Revision as of 11:17, 14 October 2021
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	==== Examples ====		==== Examples ====
	Word: '''street'''<br>		Word: '''street''' (wordcount: 2)<br>
	Embedding:~~<br~~>		Embedding 1: '''street''' -> '''activism'''
	*"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~~		*"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'''."
	*"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~~		Embedding 2: '''street''' -> '''survival'''
			*"From Brussels, the food collection was allowed so I think in the '''street'''s 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."

	Word: '''~~Companies~~'''<br>		Word: '''companies''' (wordcount: 2)<br>
	Embedding:		Embedding 1: '''companies''' -> '''crisis'''
	*"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~~		*"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."
	*"Then there was an ongoing boycott by left-wing people of the companies that closed their doors to protest this." ~~> refusal~~		Embedding 2: '''companies''' -> '''refusal'''
			*"Then there was an ongoing boycott by left-wing people of the companies that closed their doors to protest this."

	=== Step 3: Identify/generate/complexify relations ===		=== Step 3: Identify/generate/complexify relations ===

FS at 11:13, 14 October 2021

2021-10-14T11:13:08Z

← Older revision		Revision as of 11:13, 14 October 2021
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	'''A workshop with Varia'''		'''A workshop with Varia'''

	This workshop was a play on [https://en.wikipedia.org/wiki/Word2vec 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~~.		This workshop was a play on [https://en.wikipedia.org/wiki/Word2vec 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 list of number to each individual word based on what other words they find themselves in the company of. Once trained, such a model deducts synonymous words from comparing contexts, or will suggest probable words to complete partial sentence. With word2complex Varia proposed a thought experiment to resist the flattening of meaning that is inherent in such a method, trying to think about ways to keep complexity in machinic readings of situated text materials.

	[[File:Word2complex.png\|600px]]		[[File:Word2complex.png\|600px]]

FS at 11:09, 14 October 2021

2021-10-14T11:09:17Z

← Older revision		Revision as of 11:09, 14 October 2021
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	'''A workshop with Varia'''		'''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.		This workshop was a play on [https://en.wikipedia.org/wiki/Word2vec 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.

	[[File:Word2complex.png\|600px]]		[[File:Word2complex.png\|600px]]

FS at 11:08, 14 October 2021

2021-10-14T11:08:32Z

← Older revision		Revision as of 11:08, 14 October 2021
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	__NOTOC__		__NOTOC__

	== Word2complex ==		== Word2complex ==