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

TL;DR

This paper introduces Marve, a system that extracts measurement data and context from natural language text, aiding scientific analysis and decision-making in Earth science and space missions.

Contribution

Marve combines CRF-based extraction with rule-based contextual analysis, focusing on measurement context, and demonstrates high precision and recall in scientific text applications.

Findings

High-precision measurement extraction demonstrated

Effective in refining NASA's HyspIRI measurement requirements

Facilitates scientific discovery and algorithm development

Abstract

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 refining 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. These extractions accelerate broad, cross-cutting research and expose scientists new algorithmic approaches and experimental nuances. They also facilitate identification of scientific opportunities enabled by HyspIRI leading to more efficient scientific investment and research.