Collecting spatial information for locations in a text-to-scene conversion system

Masoud Rouhizadeh; Daniel Bauer; Bob Coyne; Owen Rambow; Richard Sproat

Collecting spatial information for locations in a text-to-scene conversion system

Masoud Rouhizadeh
, Daniel Bauer
, Bob Coyne
, Owen Rambow
, Richard Sproat

Linguistics

Oregon Health and Science University
Columbia University

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	16-23
Number of pages	8
Journal	CEUR Workshop Proceedings
Volume	759
State	Published - 2011
Event	Workshop on Computational Models of Spatial Language Interpretation and Generation, COSLI 2011 - In Conjunction with CogSci 2011 - Boston, MA, United States Duration: Jul 20 2011 → Jul 20 2011

Keywords

Amazon Mechanical Turk
Description corpora
Lexical resources
Location information
Text-to-scene systems
VigNet

Cite this

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title = "Collecting spatial information for locations in a text-to-scene conversion system",

abstract = "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.",

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author = "Masoud Rouhizadeh and Daniel Bauer and Bob Coyne and Owen Rambow and Richard Sproat",

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language = "English",

volume = "759",

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TY - JOUR

T1 - Collecting spatial information for locations in a text-to-scene conversion system

AU - Rouhizadeh, Masoud

AU - Bauer, Daniel

AU - Coyne, Bob

AU - Rambow, Owen

AU - Sproat, Richard

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

KW - Amazon Mechanical Turk

KW - Description corpora

KW - Lexical resources

KW - Location information

KW - Text-to-scene systems

KW - VigNet

UR - https://www.scopus.com/pages/publications/84890924639

M3 - Conference article

AN - SCOPUS:84890924639

SN - 1613-0073

VL - 759

SP - 16

EP - 23

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - Workshop on Computational Models of Spatial Language Interpretation and Generation, COSLI 2011 - In Conjunction with CogSci 2011

Y2 - 20 July 2011 through 20 July 2011

ER -

Collecting spatial information for locations in a text-to-scene conversion system

Abstract

Keywords

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