{"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/measurement-context-extraction-from-text","title":"Measurement Context Extraction from Text: Discovering Opportunities and Gaps in Earth Science","arxiv_id":"1710.04312","date":"2017-10-11","proceeding":null,"authors":["Kyle Hundman","Chris A. Mattmann"],"abstract":"We propose Marve, a system for extracting measurement values, units, and\nrelated words from natural language text. Marve uses conditional random fields\n(CRF) to identify measurement values and units, followed by a rule-based system\nto find related entities, descriptors and modifiers within a sentence. Sentence\ntokens are represented by an undirected graphical model, and rules are based on\npart-of-speech and word dependency patterns connecting values and units to\ncontextual words. Marve is unique in its focus on measurement context and early\nexperimentation demonstrates Marve's ability to generate high-precision\nextractions with strong recall. We also discuss Marve's role in refining\nmeasurement requirements for NASA's proposed HyspIRI mission, a hyperspectral\ninfrared imaging satellite that will study the world's ecosystems. In general,\nour work with HyspIRI demonstrates the value of semantic measurement\nextractions in characterizing quantitative discussion contained in large\ncorpuses of natural language text. These extractions accelerate broad,\ncross-cutting research and expose scientists new algorithmic approaches and\nexperimental nuances. They also facilitate identification of scientific\nopportunities enabled by HyspIRI leading to more efficient scientific\ninvestment and research.","url_abs":"http://arxiv.org/abs/1710.04312v1","url_pdf":"http://arxiv.org/pdf/1710.04312v1.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":"measurement-context-extraction-from-text","repo_url":"https://github.com/khundman/marve","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}