{"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/from-characters-to-time-intervals-new","title":"From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations","arxiv_id":null,"date":"2018-01-01","proceeding":"TACL 2018 1","authors":["Egoitz Laparra","Dongfang Xu","Steven Bethard"],"abstract":"This paper presents the first model for time normalization trained on the SCATE corpus. In the SCATE schema, time expressions are annotated as a semantic composition of time entities. This novel schema favors machine learning approaches, as it can be viewed as a semantic parsing task. In this work, we propose a character level multi-output neural network that outperforms previous state-of-the-art built on the TimeML schema. To compare predictions of systems that follow both SCATE and TimeML, we present a new scoring metric for time intervals. We also apply this new metric to carry out a comparative analysis of the annotations of both schemes in the same corpus.","url_abs":"https://aclanthology.org/Q18-1025","url_pdf":"https://aclanthology.org/Q18-1025.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":"from-characters-to-time-intervals-new","repo_url":"https://github.com/clulab/timenorm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"semantic-composition","task_name":"Semantic Composition"},{"task_slug":"semantic-parsing","task_name":"Semantic Parsing"},{"task_slug":"timex-normalization","task_name":"Timex normalization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/timex-normalization-on-pnt","task":"Timex normalization","dataset":"PNT","model":"Laparra et al.","rank_in_archive_order":1,"of":3,"metrics":{"F1-Score":"0.764"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}