Recognition of Implicit Geographic Movement in Text

TL;DR

This paper introduces a novel approach to automatically identify implicit geographic movement in text by creating a labeled corpus using iterative human and machine learning techniques, enhancing spatial cognition research.

Contribution

The study develops an innovative iterative labeling process combining human input, crowd voting, and machine learning to generate a large corpus for geographic movement detection in text.

Findings

Prediction accuracy achieved is sufficient for large-scale corpus creation.

Combines word embeddings with machine learning and ensembling for improved prediction.

Creates a valuable resource for geographic and spatial cognition research.

Abstract

Analyzing the geographic movement of humans, animals, and other phenomena is a growing field of research. This research has benefited urban planning, logistics, animal migration understanding, and much more. Typically, the movement is captured as precise geographic coordinates and time stamps with Global Positioning Systems (GPS). Although some research uses computational techniques to take advantage of implicit movement in descriptions of route directions, hiking paths, and historical exploration routes, innovation would accelerate with a large and diverse corpus. We created a corpus of sentences labeled as describing geographic movement or not and including the type of entity moving. Creating this corpus proved difficult without any comparable corpora to start with, high human labeling costs, and since movement can at times be interpreted differently. To overcome these challenges, we developed an iterative process employing hand labeling, crowd voting for confirmation, and machine learning to predict more labels. By merging advances in word embeddings with traditional machine learning models and model ensembling, prediction accuracy is at an acceptable level to produce a large silver-standard corpus despite the small gold-standard corpus training set. Our corpus will likely benefit computational processing of geography in text and spatial cognition, in addition to detection of movement.