Any-gram Kernels for Sentence Classification: A Sentiment Analysis Case Study
This work addresses sentiment analysis for text classification, but it is incremental as it builds upon existing any-gram kernels.
The authors tackled sentiment classification by proposing a more efficient and effective any-gram kernel method that improves performance, achieving significantly better results in sentiment analysis.
Any-gram kernels are a flexible and efficient way to employ bag-of-n-gram features when learning from textual data. They are also compatible with the use of word embeddings so that word similarities can be accounted for. While the original any-gram kernels are implemented on top of tree kernels, we propose a new approach which is independent of tree kernels and is more efficient. We also propose a more effective way to make use of word embeddings than the original any-gram formulation. When applied to the task of sentiment classification, our new formulation achieves significantly better performance.