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Ríos-Gaona, M., Gelbukh, A., & Bandyopadhyay, S. (2012). Recognizing Textual Entailment Using a Machine Learning Approach. In Mexican International Conference on Artificial Intelligence (pp. 177-185). Berlin: Springer.
https://doi.org/10.1007/978-3-642-16773-7_15

has been cited by the following article:

• TITLE: Development of Answer Validation System Using Responders’ Attributes and Crowd Ranking

  AUTHORS: Mercy Adebisi, Bolanle Ojokoh, Tolulope Adebayo, Akintoba Akinwonmi, Fatai Sunmola

  KEYWORDS: Askers, Answerers, Community Question Answering (CQA), Question Answering (QA), Crowd Sourcing, Answer Validation

  JOURNAL NAME: Journal of Service Science and Management, Vol.14 No.3, June 30, 2021

  ABSTRACT: Crowdsourcing has found a wide range of application in Community Question Answering (CQA). However, one of its biggest challenges is the need to address the quality of crowd answers contributions. Therefore, this work proposed a system that seeks to validate answers to questions provided by respondents using responders’ attributes and crowd ranking technique. Weights were assigned to respondent answers based on their academic records, experience and understanding of the question to obtain valid answers. Thereafter, valid answers were ranked by the crowd using Borda Count algorithm. The proposed system was evaluated using Usability and User experience (UX) measurement. The result obtained demonstrated the effectiveness of the applied technique.