In the previous week, we used different classification methods to assign documents to categories. We trained our classifiers on the word vectors of the documents in our training dataset. In the section on relation extraction via supervised learning, Jurafsky and Martin point to a number of different features beyond mere word vectors that can be used for this type of classification task such as word positions or dependency tree paths. I wonder how exactly we simultaneously incorporate these features into our data structure or, put in practical terms: how do the vectors look like that we feed a classifier with for supervised relation extraction.
In the previous week, we used different classification methods to assign documents to categories. We trained our classifiers on the word vectors of the documents in our training dataset. In the section on relation extraction via supervised learning, Jurafsky and Martin point to a number of different features beyond mere word vectors that can be used for this type of classification task such as word positions or dependency tree paths. I wonder how exactly we simultaneously incorporate these features into our data structure or, put in practical terms: how do the vectors look like that we feed a classifier with for supervised relation extraction.