{"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/detecting-and-explaining-causes-from-text-for","title":"Detecting and Explaining Causes From Text For a Time Series Event","arxiv_id":"1707.08852","date":"2017-07-27","proceeding":"EMNLP 2017 9","authors":["Dongyeop Kang","Varun Gangal","Ang Lu","Zheng Chen","Eduard Hovy"],"abstract":"Explaining underlying causes or effects about events is a challenging but\nvaluable task. We define a novel problem of generating explanations of a time\nseries event by (1) searching cause and effect relationships of the time series\nwith textual data and (2) constructing a connecting chain between them to\ngenerate an explanation. To detect causal features from text, we propose a\nnovel method based on the Granger causality of time series between features\nextracted from text such as N-grams, topics, sentiments, and their composition.\nThe generation of the sequence of causal entities requires a commonsense\ncausative knowledge base with efficient reasoning. To ensure good\ninterpretability and appropriate lexical usage we combine symbolic and neural\nrepresentations, using a neural reasoning algorithm trained on commonsense\ncausal tuples to predict the next cause step. Our quantitative and human\nanalysis show empirical evidence that our method successfully extracts\nmeaningful causality relationships between time series with textual features\nand generates appropriate explanation between them.","url_abs":"http://arxiv.org/abs/1707.08852v1","url_pdf":"http://arxiv.org/pdf/1707.08852v1.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":"detecting-and-explaining-causes-from-text-for","repo_url":"https://github.com/dykang/cgraph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.08852","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}