Hedge detection as a lens on framing in the GMO debates: A position paper
Table 3: Number of features.
['Feature source', '#features']
We experimented on them separately (Bio, Wiki). Also, to make our classifier more generalizable to different datasets, we also trained models based on the two datasets combined (Bio+Wiki). The basic feature set was the frequency of each hedge cue word from the training corpus after removing stop words and punctuation and transforming words to lowercase. Then, we extracted unigrams, bigrams and trigrams from each hedge cue phrase. Notice that there are many more features in Wiki. As mentioned above, in Wiki, some cues are as specific as ‘some of schumann’s best choral writing’, ‘people of the jewish tradition’, or ‘ certain leisure or cultural activities’. Taking n-grams from such specific cues can cause some sentences to be classified incorrectly.