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dos.4 Predicting resemblance judgments off embedding spaces
Specific studies (Schakel & Wilson, 2015 ) possess exhibited a relationship between your regularity that a term appears on degree corpus therefore the amount of the phrase vector
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So you’re able to assume resemblance anywhere between a couple things when you look at the an enthusiastic embedding area, i calculated the new cosine length amongst the word vectors comparable to per target. We put cosine distance because the a good metric for 2 factors why. Earliest, cosine range is a generally claimed metric utilized in the newest literature which allows having direct investigations so you can earlier works (Baroni ainsi que al., 2014 ; Mikolov, Chen, ainsi que al., 2013 ; Mikolov, Sutskever, et al., 2013 ; Pennington ainsi que al., 2014 ; Pereira mais aussi al., 2016 ). 2nd, cosine range disregards the exact distance or magnitude of the two vectors are opposed, taking into consideration precisely the angle amongst the vectors. That frequency relationship shouldn’t have hit to the semantic resemblance of these two terms, using a radius metric such as for example cosine length that ignores magnitude/duration data is wise.
