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Word2Vec embedding model trained on Czech legislation (from April 2020) corpus using gensim implementation with the following parameters in addition to default settings: vector dimension = \(400\), window size = \(10\), word minimum count = \(10\), sample = \(10^{-5}\).
This work was supported by the Czech Science Foundation (GAČR),grant number 19-01641S.
model, word2vec, word embedding, czech legislation, gensim
model, word2vec, word embedding, czech legislation, gensim
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citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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