Well, Actually not only the text vector but the context vectors are reduced to have 100 dimensions.

- Context Vectors : they tend to be quite huge, thus lets say if you have a vector for the topic `microsoft` it could easily have 1000 dimensions. In order to give more relevance to the ones with higher counts we reduced the dimension to the 100 more common relevant dimenisons + dimensions which overlap with the text vector. Otherwise lets say a menainguful word for `microsoft` such as `Windows` might be reduced by the long tail of context words dimensions.

- Text Vector: It is a similar explanation to the previous one. In fact it is the same.

I have to admit I just set this at the beginning and never tried with different values.