text: 52,70,1261100792729071616,3260753346,Deepanway Ghosal,"Excited to share our new #acl2020nlp paper KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis with @hdevamanyu @soujanyaporia @radamihalcea, Abhinaba Roy, and Navonil Majumder. Paper: <LINK> Code: <LINK> #NLProc <LINK> In this work, we avail commonsense knowledge from ConceptNet to perform domain adaptation and show the ability of graph features helping in bridging domain gap between seen and unseen domains.",https://arxiv.org/abs/2005.00791,"Cross-domain sentiment analysis has received significant attention in recent years, prompted by the need to combat the domain gap between different applications that make use of sentiment analysis. In this paper, we take a novel perspective on this task by exploring the role of external commonsense knowledge. We introduce a new framework, KinGDOM, which utilizes the ConceptNet knowledge graph to enrich the semantics of a document by providing both domain-specific and domain-general background concepts. These concepts are learned by training a graph convolutional autoencoder that leverages inter-domain concepts in a domain-invariant manner. Conditioning a popular domain-adversarial baseline method with these learned concepts helps improve its performance over state-of-the-art approaches, demonstrating the efficacy of our proposed framework. ",KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis,2,"['Excited to share our new #acl2020nlp paper KinGDOM: Knowledge-Guided DOMain adaptation for sentiment analysis with @hdevamanyu @soujanyaporia @radamihalcea, Abhinaba Roy, and Navonil Majumder.\nPaper: <LINK>\nCode: <LINK>\n#NLProc <LINK>', 'In this work, we avail commonsense knowledge from ConceptNet to perform domain adaptation and show the ability of graph features helping in bridging domain gap between seen and unseen domains.']",20,05,427