A Chinese Dataset with Negative Full Forms for General Abbreviation Prediction
Table 3: Results on comparing different methods on generalized abbreviations.
['Method', 'Discriminate Acc(%)', 'Overall All-Acc(%)', 'Overall Char-Acc(%)']
In the table, the overall accuracy is most important and it means the final accuracy achieved by the systems in generalized abbreviation prediction with NFFs. For the completeness of experimental information, we also show the discriminate accuracy. The discriminate accuracy checks the accuracy of discriminating positive and negative full forms, without comparing the generated abbreviations with the gold-standard abbreviations. The CRF model outperforms the MEMM and averaged perceptron models. The CRF model achieves best overall character accuracy. BLSTM outperforms other models in both discriminate accuracy and all-match accuracy. However, training a neural network always needs a large amount of data. With a dataset that is not so large, the ability of a neural network may be limited.