Geo-Coding for the Mapping of Documents and Social Media Messages

Abstract

Many places on the earth have the same name, so it is difficult to determine which written place is meant. This research aims to improve the precision of geo-coding by using natural language processing and machine learning techniques (SVM specifically). We used data that was already geo-coded the ACE Spatial ML data set, and a large tweet set in which tweets were selected for having GPS locations (that we could use to improve validity). The report details our methods for text and microtext.

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Document Details

Document Type
Technical Report
Publication Date
Aug 22, 2013
Accession Number
ADA595532

Entities

People

  • Judith Gelernter

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Computational Linguistics
  • Computer Programming
  • Data Sets
  • Errors
  • Information Retrieval
  • Language
  • Linguistics
  • Machine Learning
  • Natural Language Processing
  • Natural Languages
  • Network Science
  • Social Media
  • Standards
  • Students
  • Word Processors

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Distributed Systems and Data Platform Development
  • Library and Information Science

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks
  • Space