Part-Of-Speech Tag Embedding for Modeling Sentences and Documents
Dong Yu · eScholarship (California Digital Library) · 2016
In sentence modeling, neural network approaches that leverage the tree-structural features of sentences have recently achieved state-of-the-art results. However, such approaches require complex architectures and are not easily extensible to document modeling. In this paper, we propose a very simple convolutional neural network model that incorporates Part-Of-Speech tag information (PCNN). While our model can be easily extensible to document modeling, it shows great performance on both sentence and document modeling tasks. As a result of sentiment analysis and question classification tasks, PCNN achieves the performance comparable to that of other more complex state-of-the-art models on sentence modeling and outperforms them on document modeling. We also make efforts to explore the effect of POS tag embeddings more thoroughly by conducting various experiments.