Request Correction
Use this form to request corrections to the paper metadata. Select the fields that need correction and provide the correct information.
Correction Guidelines
- Click the edit button next to a field to report a correction.
- Fill in the suggested correction value for each field you want to correct.
- Provide your name and email so we can contact you if needed.
Correction requests are currently disabled.
Paper Information
Can the Crowd be Controlled?: A Case Study on Crowd Sourcing and Automatic Validation of Completed Tasks based on User Modeling
Paper Fields
Click the edit button next to a field to report a correction.
Can the Crowd be Controlled?: A Case Study on Crowd Sourcing and Automatic Validation of Completed Tasks based on User Modeling
Annotation is an essential step in the development cycle of many Natural Language Processing (NLP) systems. Lately, crowd-sourcing has been employed to facilitate large scale annotation at a reduced cost. Unfortunately, verifying the quality of the submitted annotations is a daunting task. Existing approaches address this problem either through sampling or redundancy. However, these approaches do have a cost associated with it. Based on the observation that a crowd-sourcing worker returns to do a task that he has done previously, a novel framework for automatic validation of crowd-sourced task is proposed in this paper. A case study based on sentiment analysis is presented to elucidate the framework and its feasibility. The result suggests that validation of the crowd-sourced task can be automated to a certain extent.
Authors
Expand an author to correct their information. Use the remove button to request author removal, or add a new author.
PDF Attachment
You may attach a PDF as a corrected version of the paper. Max file size: 10MB. Only PDF files are accepted.
Your Information
Author Declaration *
Select at least one field to correct using the edit buttons above.