Abstract
As an emerging text classification task, stance detection is much helpful in reviewing subjective text and mining expressed attitudes of a person or organization towards an object. Due to the similarity with other text classification tasks, stance detection is always tackled by conventional classification methods. However, there is a big difference between stance detection and others, since stance detection depends much on human background knowledge while others do not. Therefore, to address such a unique problem, we propose a novel method, which leverages knowledge graph and incorporates text-mentioned knowledge with a deep classifier, by a key component named Opinion-aware Knowledge Embedding (OKE). The proposed OKE can integrate the objective knowledge facts and subjective text opinion well by a customized and effective attention mechanism. Our experiments also show that the proposed method comprehensively outperforms all the baselines on real datasets.
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This work is supported by State Grid Technical Project (No. 52110418002W).
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Xu, Z., Li, Q., Chen, W., Cui, Y., Qiu, Z., Wang, T. (2019). Opinion-Aware Knowledge Embedding for Stance Detection. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11642. Springer, Cham. https://doi.org/10.1007/978-3-030-26075-0_26
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