Abstract
Extracting aspects from opinion reviews is an essential task of fine-grained sentiment analysis. In this paper, we introduce outer product of dependency-based word vectors and specialized features as representation of words. With such extended embeddings composed in recurrent neural networks, we make use of advantages of both word embeddings and traditional features. Evaluated on SemEval 2014 task 4 dataset, the proposed method outperform existing recurrent models based methods, achieving a result comparable with the state-of-the-art method. It shows that it is an effective way to achieve better extraction performance by improving word representations.
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Acknowledgments
This work is supported by the projects of China Postdoctoral Science Special Foundation (No. 2014T70340), National Natural Science Foundation of China (No. 61300114 and No. 61572151), and Specialized Research Fund for the Doctoral Program of Higher Education (No. 20132302120047).
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Wang, X., Liu, Y., Sun, C., Liu, M., Wang, X. (2016). Extended Dependency-Based Word Embeddings for Aspect Extraction. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_13
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DOI: https://doi.org/10.1007/978-3-319-46681-1_13
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