English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We propose Marve, a system for extracting measurement values, units, andrelated words from natural language text. Marve uses conditional random fields(CRF) to identify measurement values and units, followed by a rule-based systemto find related entities, descriptors and modifiers within a sentence. Sentencetokens are represented by an undirected graphical model, and rules are based onpart-of-speech and word dependency patterns connecting values and units tocontextual words. Marve is unique in its focus on measurement context and earlyexperimentation demonstrates Marve's ability to generate high-precisionextractions with strong recall. We also discuss Marve's role in refiningmeasurement requirements for NASA's proposed HyspIRI mission, a hyperspectralinfrared imaging satellite that will study the world's ecosystems. In general,our work with HyspIRI demonstrates the value of semantic measurementextractions in characterizing quantitative discussion contained in largecorpuses of natural language text. These extractions accelerate broad,cross-cutting research and expose scientists new algorithmic approaches andexperimental nuances. They also facilitate identification of scientificopportunities enabled by HyspIRI leading to more efficient scientificinvestment and research.

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