The Influence of Context on Dialog Act Recognition
Table 7: Accuracy (%) results obtained on the 42-label Switchboard corpus using an SVM classifier with n-gram frequencies as features. The first row presents results obtained using a specific n-gram length, while the second presents results obtained using a cumulative n, that is, using all n-grams with n between 1 and the pivot of the column.
['[EMPTY]', 'n 1', 'n 2', 'n 3', 'n 4', 'n 5']
Base Features We used the frequencies of word n-grams as the main features extracted from the current segment, as n-grams are able to capture keywords and word sequence information. In order to select which n-grams to use, we performed experiments using a specific n-gram length, between 1 and 5, as well as using a cumulative n, also between 1 and 5. We can see that using both unigrams and bigrams led to the best results. However, along each row, the result differences are not statistically significant. Nonetheless, for n larger than 2, there is statistical significance in the difference between the results obtained using a specific n and a cumulative n . This means that the information provided by unigrams and bigrams is relevant. Thus, the remaining experiments presented in this article used unigrams and bigrams as features.