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Brief Literature Review
Welcome to the word2manylanguages wiki!
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Distributional Models of Memory
a. General overviews of these models:
- https://www.researchgate.net/profile/Jon_Willits2/publication/264825283_Models_of_semantic_memory/links/53f299cd0cf2bc0c40ecadc0.pdf
- https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1124&context=psychologypub
b. The main paper I am basing a lot of this idea on: http://crr.ugent.be/papers/Mandera_et_al_JML_2016.pdf Word2vec
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What you might use as corpora for these models: The Subtitle Projects
a. http://crr.ugent.be/programs-data/subtitle-frequencies
b. https://link.springer.com/content/pdf/10.3758%252FBRM.41.4.977.pdf
c. https://www.frontiersin.org/articles/10.3389/fpsyg.2011.00027/full
d. https://biblio.ugent.be/publication/599589/file/738788.pdf
e. There are bunch of languages, but this paper brings it together: https://psyarxiv.com/fcrmy/
f. Python package: https://github.com/jvparidon/subs2vec
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What you might use as dependent variables to assess if these models "work"
a. The Lexicon Projects - in several languages, but get started with English: https://link.springer.com/content/pdf/10.3758/BF03193014.pdf
b. The Semantic Priming Project: https://www.aggieerin.com/pubs/hutchison%2013.pdf
c. The Small World of Words: https://smallworldofwords.org/articles/2018.DeDeyne.The%20Small%20World%20of%20Words%20English%20word%20association%20norms%20for%20over%2012,000%20cue%20words.BRM.pdf
Goal/hypothesis/contribution to the literature:
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How comparable are models of semantic spaces built on different languages, given a similar type of corpora input?
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Is there a general rule about the dimensionality or window size that we might consider "universal"?
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Are there common pairs that occur with the most frequent words that we might consider a "universal set" for studying similarity?
Part 2: Can we make these models better by combining subtitles and simple wikipedia: https://dumps.wikimedia.org/simplewiki/20200601/