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131012 on Aug 16, 2016 | parent | context | favorite | on: Visualizing Clusters of Clickbait Headlines Using ...

"Once the dictionary is created, we can average all the word vectors for a given headline to get the numeric representation of the headline itself."

Can someone explain me why this is useful? Aren't we losing a lot of precision from word2vec results?

And as a general question: is there any useful knowledge we can extract from this vizualisation apart: most of the time, news channels write about different things?

Don't get me wrong, I think the techniques displayed here are really cool, but I have the feeling the conclusions are either absent or trite.

guidopallemans on Aug 18, 2016 [–]

> Aren't we losing a lot of precision from word2vec results?

Not really, because the vectors are really high-dimensional, and the words that occur in the same headline together usually aren't close together.

So it's not really losing precision, it's more like combining the meanings of the words (numerically).

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