Paper Info
Title
Measurement Context Extraction from Text: Discovering Opportunities and Gaps in Earth Science.
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 Marveu0027s ability to generate high-precision extractions with strong recall. We also discuss Marveu0027s role in refining measurement requirements for NASAu0027s proposed HyspIRI mission, a hyperspectral infrared imaging satellite that will study the worldu0027s 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.
Year
Venue
Field
2017
arXiv: Information Retrieval
Conditional random field,Data mining,Information retrieval,Computer science,Hyperspectral imaging,Natural language,Graphical model,Sentence
DocType
Volume
ISSN
Journal
abs/1710.04312
23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Data-Driven Discovery Workshop, Halifax, Canada, August 2017
Citations 
PageRank 
References 
0
0.34
11
Authors
2
Name
Order
Citations
PageRank
Kyle Hundman110.73
Chris A. Mattmann220025.39