Targeted question answering on smartphones utilizing app based user classification

Yilmaz, Yavuz Selim; Aydin, Bahadir Ismail; Demirbaş, Murat

doi:10.1109/cts.2014.6867591

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Thus, there are alternative solutions that can be adapted at this step. We can investigate how we can adapt the lessons learned on timing of the answers and confidence of the participants. Expert finding and selection of crowds are hot topics for crowdsourcing research, 14,71–73 and our observations discussed in Section 6.1 can provide important directives while assigning tasks to participants.…”

Section: Resultsmentioning

confidence: 99%

“…We described the design, implementation, and the deployment of Crowd-Millionaire in previous works. [12][13][14] We use this app and the data to evaluate our MCQAalgorithms defined in this work.…”

Section: Crowdmillionaire Architecturementioning

confidence: 99%

“…CrowdMillionaire consists of 2 main parts: (1) a server side for storing and dispatching the questions, collecting the answers, providing statistics, and running our MCQA algorithms, (2) a mobile side for presenting the questions to the users and letting them answer the questions. We described the design, implementation, and the deployment of CrowdMillionaire in previous works 12–14 . We use this app and the data to evaluate our MCQA algorithms defined in this work.…”

Section: Crowdmillionaire Architecturementioning

confidence: 99%

See 2 more Smart Citations

A crowdsourced “Who wants to be a millionaire?” player

Aydin

Yilmaz

Demirbaş

2017

Concurrency and Computation

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Question answering is a fundamental problem for artificial intelligence research. State-of-the-art question answering systems, such as search engines, can answer well-formed factual questions. However, they fail on nonfactual and natural language queries. On the other hand, crowdsourcing leverages human intelligence to present solutions for problems, which are hard for computers. In the process of crowdsourcing, aggregating the responses from the crowd is a big challenge of itself. This work presents a model that integrates artificial intelligence with crowdsourcing to answer difficult natural language multiple choice questions. We build a crowdsourced mobile gaming experience for "Who wants to be a millionaire?" TV quiz show to test our methods. We use lightweight artificial intelligence models in aggregating the crowd responses. Our methods are able to answer even the hardest questions with very high accuracy. The experiment results suggest that building a super player for "Who wants to be a millionaire?" is feasible using our models. This study shows that facilitating crowdsourcing with artificial intelligence is an important tool for answering questions, which trouble state-of-the-art question answering systems.

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Section: Resultsmentioning

confidence: 99%

Section: Crowdmillionaire Architecturementioning

confidence: 99%

Section: Crowdmillionaire Architecturementioning

confidence: 99%

See 1 more Smart Citation

A crowdsourced “Who wants to be a millionaire?” player

Aydin

Yilmaz

Demirbaş

2017

Concurrency and Computation

Self Cite

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Development of an Arabic Questions Answering App for Android Smartphones

Juma

Monem

Shaalan

2017

Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017

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A Mobile based Intelligent Question Answering System for Education Domain

Karpagam¹,

Saradha²

2018

IJIEEB

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Abstract-The domain of intelligent question answering systems is leading the major role in fulfill the user requirement with specific answers stimulate QA research to the next level with machine learning techniques. In this paper, we present mobile based question answering system acts as a personal assistant in learning and for providing the user with information on computers, software and hardware, book reviews by using natural language for the communications. The proposed Mobile based QA models will accept the natural language query, analysis and match them with information stored in the knowledge base and display the optimized result. The knowledge base created from the benchmark data set such as Amazon book reviews, 20newsgroup and Yahoo! Answer data set clustered with content specific clustering and displays the outcome in the form of snippets as output. Sentiment analysis used to decrease the vocabulary gap among the user query and retrieved candidate answer solutions. The results of the proposed interface evaluated with standard metrics such as Precision, Recall, F1-Score, Inverse precision and Inverse recall for the appropriate return of relevant answer.

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Targeted question answering on smartphones utilizing app based user classification

Cited by 3 publications

References 9 publications

A crowdsourced “Who wants to be a millionaire?” player

A crowdsourced “Who wants to be a millionaire?” player

Development of an Arabic Questions Answering App for Android Smartphones

A Mobile based Intelligent Question Answering System for Education Domain

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