Marve: Measurement Context Extraction from Text

We propose Marve, a system for extracting measurement values,
units, and related words from natural language text. Marve uses
conditional random fields (CRF) to identify measurement values
and units, followed by a rule-based system to find related entities,
descriptors and modifiers within a sentence. Sentence tokens are
represented by an undirected graphical model, and rules are based
on part-of-speech and word dependency patterns 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 justifying NASA JPL’s proposed
HyspIRI mission, a hyper spectral 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. These extractions accelerate broad-cross
cu ing literature surveys and expose researchers and scientists new
algorithmic approaches and experimental nuances. They also facilitate
identification of scientific opportunities enabled by HyspIRI
leading to more informed scientific investment and research.