Journal
PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE 2021 (WWW 2021)
Volume -, Issue -, Pages 3453-3464Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3442381.3449790
Keywords
cross-target stance detection; graph networks; opinion mining
Categories
Funding
- National Natural Science Foundation of China [61632011, 61876053, 62006062]
- Guangdong Province Covid-19 Pandemic Control Research Funding [2020KZDZX1224]
- Shenzhen Foundational Research Funding [JCYJ20180507183527919, JCYJ20180507183608379]
- China Postdoctoral Science Foundation [2020M670912]
- EPSRC [EP/T017112/1, EP/V048597/1]
- Natural Science Foundation of Guangdong Province of China [2019A1515011705]
- Youth Innovation Promotion Association of CAS China
- Turing AI Fellowship - UK Research and Innovation (UKRI) [EP/V020579/1]
- EPSRC [EP/V020579/1] Funding Source: UKRI
Ask authors/readers for more resources
Target plays a crucial role in stance detection, and handling unknown targets is a significant challenge. This paper introduces a novel approach to achieve stance detection across different targets, by constructing target-adaptive dependency graphs specific to each target.
Target plays an essential role in stance detection of an opinionated review/claim, since the stance expressed in the text often depends on the target. In practice, we need to deal with targets unseen in the annotated training data. As such, detecting stance for an unknown or unseen target is an important research problem. This paper presents a novel approach that automatically identifies and adapts the target-dependent and target-independent roles that a word plays with respect to a specific target in stance expressions, so as to achieve cross-target stance detection. More concretely, we explore a novel solution of constructing heterogeneous target-adaptive pragmatics dependency graphs (TPDG) for each sentence towards a given target. An in-target graph is constructed to produce inherent pragmatics dependencies of words for a distinct target. In addition, another cross-target graph is constructed to develop the versatility of words across all targets for boosting the learning of dominant word-level stance expressions available to an unknown target. A novel graph-aware model with interactive Graphical Convolutional Network (GCN) blocks is developed to derive the target-adaptive graph representation of the context for stance detection. The experimental results on a number of benchmark datasets show that our proposed model outperforms state-of-the-art methods in cross-target stance detection.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available