Mimicking Human Process: Text Representation via Latent Semantic Clustering for Classification1footnote 11footnote 1This work was presented at 2nd Workshop on Humanizing AI (HAI) at IJCAI’19 in Macao, China
Table 2: Accuracy of all the models on the five datasets. The result marked with ⋄ is re-printed from wang2018densely (wang2018densely).
['Method', 'AGNews', 'Yah.A.', 'DBPedia', 'Yelp P.', 'Yelp F.']
The overall performance is competitive. It can be seen from the table that our method improves the best performance by 1.0%, 0.1% and 2.5% on Yah A., Yelp P. and Yelp F respectively and is comparable on AGNews. Compared with the baselines, our results exceed the traditional feature-based models (n-gram TF-IDF, BoW), the word embedding based representation models (FastText, SWEM) and LEAM by a large margin. Furthermore, we gain great improvement over the LSTM-based models. Though our model and the self-attentive model all aim at getting multiple vectors indicating different semantic information, as our model gather the words into clusters to enrich the representations, we achieve a better performance over theirs. Compared with the deep CNNs, our shallow model with a relatively simpler structure outperforms them as well.