Deep Short Text Classification with Knowledge Powered Attention
Table 1: Details of the experimental datasets.
['Datasets', '# Class', 'Training/Validation/Test set', 'Avg. Chars', 'Avg. Words', 'Avg. Ent', 'Avg. Con']
There are 7 kinds of emotions in these weibos, such as anger, disgust, fear and etc. The polarity of each review is binary, either positive or negative. dataset with 18 classes (e.g., entertainment, game, food) from NLPCC2017 The average word length of the above-mentioned three datasets is over 12. To test whether our model works on much shorter texts, we build the Topic dataset whose average word length is 7.99. The Topic dataset is collected from Sogou news We acquire the title as short text and topic as label. We use conceptual information as prior knowledge to enrich the representation of short text and improve the performance of classification. To verify the power of knowledge in our model, we pick some testing examples from Topic dataset and illustrate them in Table LABEL:table:case3. These short texts are correctly classified by our model but misclassified by traditional DNNs that do not use any knowledge. In general, the conceptual information plays a crucial role in short text classification, especially when the context of short texts is not enough. As the first example shown in Table LABEL:table:case3, Revolution of 1911 is a rare word, i.e., occurs less frequently in the training set, and thus is difficult to learn a good representation, resulting in poor performance of traditional DNNs. The concepts such as history and historical event used in our model are helpful for classifying the short text into the correct class history.