{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-ideal-point-estimation-network","title":"Neural Ideal Point Estimation Network","arxiv_id":"1904.11727","date":"2019-04-26","proceeding":null,"authors":["Kyungwoo Song","Wonsung Lee","Il-Chul Moon"],"abstract":"Understanding politics is challenging because the politics take the influence\nfrom everything. Even we limit ourselves to the political context in the\nlegislative processes; we need a better understanding of latent factors, such\nas legislators, bills, their ideal points, and their relations. From the\nmodeling perspective, this is difficult 1) because these observations lie in a\nhigh dimension that requires learning on low dimensional representations, and\n2) because these observations require complex probabilistic modeling with\nlatent variables to reflect the causalities. This paper presents a new model to\nreflect and understand this political setting, NIPEN, including factors\nmentioned above in the legislation. We propose two versions of NIPEN: one is a\nhybrid model of deep learning and probabilistic graphical model, and the other\nmodel is a neural tensor model. Our result indicates that NIPEN successfully\nlearns the manifold of the legislative bill texts, and NIPEN utilizes the\nlearned low-dimensional latent variables to increase the prediction performance\nof legislators' votings. Additionally, by virtue of being a domain-rich\nprobabilistic model, NIPEN shows the hidden strength of the legislators' trust\nnetwork and their various characteristics on casting votes.","url_abs":"http://arxiv.org/abs/1904.11727v1","url_pdf":"http://arxiv.org/pdf/1904.11727v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neural-ideal-point-estimation-network","repo_url":"https://github.com/gtshs2/NIPEN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}