Measurement Context Extraction from Text: Discovering Opportunities and Gaps in Earth Science

We propose Marve, a system for extracting measurement values, units, and related words from natural language text. Marve uses conditional random €elds (CRF) to identify measurement values and units, followed by a rule-based system to €nd related entities, descriptors and modi€ers within a sentence. Sentence tokens are represented by an undirected graphical model, and rules are based on part-of-speech and word dependency paŠerns connecting values and units to contextual words. Marve is unique in its focus on measurement context and early experimentation demonstrates Marve’s ability to generate high-precision extractions with strong recall. We also discuss Marve’s role in re€ning measurement requirements for NASA’s proposed HyspIRI mission, a hyperspectral infrared imaging satellite that will study the world’s ecosystems. In general, our work with HyspIRI demonstrates the value of semantic measurement extractions in characterizing quantitative discussion contained in large corpuses of natural language text. Œese extractions accelerate broad, cross-cuŠing research and expose scientists new algorithmic approaches and experimental nuances. Œey also facilitate identi€cation of scienti€c opportunities enabled by HyspIRI leading to more ecient scienti€c investment and research.