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arXiv · 1712.07004

Any-gram Kernels for Sentence Classification: A Sentiment Analysis Case Study

Rasoul Kaljahi·Jennifer Foster
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Abstract

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.

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BibTeXRIS

Rasoul Kaljahi, Jennifer Foster. 2017-12-19. Any-gram Kernels for Sentence Classification: A Sentiment Analysis Case Study. https://arxiv.org/abs/1712.07004

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