{"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/no-permanent-friends-or-enemies-tracking","title":"No Permanent Friends or Enemies: Tracking Relationships between Nations from News","arxiv_id":"1904.08950","date":"2019-04-18","proceeding":"NAACL 2019 6","authors":["Xiaochuang Han","Eunsol Choi","Chenhao Tan"],"abstract":"Understanding the dynamics of international politics is important yet\nchallenging for civilians. In this work, we explore unsupervised neural models\nto infer relations between nations from news articles. We extend existing\nmodels by incorporating shallow linguistics information and propose a new\nautomatic evaluation metric that aligns relationship dynamics with manually\nannotated key events. As understanding international relations requires\ncarefully analyzing complex relationships, we conduct in-person human\nevaluations with three groups of participants. Overall, humans prefer the\noutputs of our model and give insightful feedback that suggests future\ndirections for human-centered models. Furthermore, our model reveals\ninteresting regional differences in news coverage. For instance, with respect\nto US-China relations, Singaporean media focus more on \"strengthening\" and\n\"purchasing\", while US media focus more on \"criticizing\" and \"denouncing\".","url_abs":"http://arxiv.org/abs/1904.08950v1","url_pdf":"http://arxiv.org/pdf/1904.08950v1.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":"no-permanent-friends-or-enemies-tracking","repo_url":"https://github.com/BoulderDS/LARN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.08950","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}