Representing Knowledge Gaps Effectively

Belasco, Alan; Curtis, Jon; Kahlert, Robert C.; Klein, Charles J.; Mayans, Corinne; Reagan, Pace

doi:10.1007/978-3-540-30545-3_15

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…In addition, there are projects such as Open Mind Common Sense (OMCS) (Stork, 1999;Singh et al, 2002), ISI LEARNER (Chklovski, 2003), and the Fact Entry Tool by Cycorp (Belasco et al, 2002) where knowledge is gathered from volunteers.…”

Section: Introductionmentioning

confidence: 99%

Acquiring high quality non-expert knowledge from on-demand workforce

Feng¹,

Besana²,

Zajac³

2009

Proceedings of the 2009 Workshop on the People's Web Meets NLP Collaboratively Constructed Semantic Resources - People's Web '0

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Being expensive and time consuming, human knowledge acquisition has consistently been a major bottleneck for solving real problems. In this paper, we present a practical framework for acquiring high quality non-expert knowledge from on-demand workforce using Amazon Mechanical Turk (MTurk). We show how to apply this framework to collect large-scale human knowledge on AOL query classification in a fast and efficient fashion. Based on extensive experiments and analysis, we demonstrate how to detect low-quality labels from massive data sets and their impact on collecting high-quality knowledge. Our experimental findings also provide insight into the best practices on balancing cost and data quality for using MTurk.

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Section: Introductionmentioning

confidence: 99%

Acquiring high quality non-expert knowledge from on-demand workforce

Feng¹,

Besana²,

Zajac³

2009

Proceedings of the 2009 Workshop on the People's Web Meets NLP Collaboratively Constructed Semantic Resources - People's Web '0

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“…There are many examples of AI applications which use human input data. In [33], the users are presented with the annotated examples and asked to use them to predict the rating of the target users on other items. In Natural language processing (NLP), a computer system needs to understand and process human language.…”

Section: Human Computation For Artificial Intelligencementioning

confidence: 99%

A Study of Human Computation Applications

Yuen

2018

ABBA

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Human computation is a technique that utilizes human abilities to perform computation tasks that are difficult for computers to process. Since the concept of human computation was introduced in 2003, many applications have been developed and the output data collected in these applications is very useful for many artificial intelligent applications. In this paper, we categorize existing human computation applications and present their major objectives. This paper gives a better understanding on how human contribute to AI applications.

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Acquisition of Commonsense Knowledge

Mueller

2015

Commonsense Reasoning

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Representing Knowledge Gaps Effectively

Cited by 5 publications

References 2 publications

Acquiring high quality non-expert knowledge from on-demand workforce

Acquiring high quality non-expert knowledge from on-demand workforce

A Study of Human Computation Applications

Acquisition of Commonsense Knowledge

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