Academic Commons

Presentations (Communicative Events)

Collecting Spatial Information for Locations in a Text-to-Scene Conversion System

Rouhizadeh, Masoud; Bauer, Daniel; Coyne, Robert Eric; Rambow, Owen C.; Sproat, Richard

We investigate using Amazon Mechanical Turk (AMT) for building a low-level description corpus and populating VigNet, a comprehensive semantic resource that we will use in a text-to-scene generation system. To depict a picture of a location, VigNet should contain the knowledge about the typical objects in that location and the arrangements of those objects. Such information is mostly common-sense knowledge that is taken for granted by human beings and is not stated in existing lexical resources and in text corpora. In this paper we focus on collecting objects of locations using AMT. Our results show that it is a promising approach.

Files

  • thumnail for cosli-2011-AMT_locations-final.pdf cosli-2011-AMT_locations-final.pdf application/pdf 222 KB Download File

More About This Work

Academic Units
Computer Science
Center for Computational Learning Systems
Publisher
CoSLI-2 (Computational Models for Spatial Languages) at CogSci 2011
Published Here
August 2, 2013
Academic Commons provides global access to research and scholarship produced at Columbia University, Barnard College, Teachers College, Union Theological Seminary and Jewish Theological Seminary. Academic Commons is managed by the Columbia University Libraries.