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A Question Routing Technique Using Deep Neural Network for Communities of Question Answering
- Source :
- Database Systems for Advanced Applications ISBN: 9783319557526, DASFAA (1)
- Publication Year :
- 2017
- Publisher :
- Springer International Publishing, 2017.
-
Abstract
- Online Communities for Question Answering (CQA) such as Quora and Stack Overflow face the challenge of providing high quality answers to the questions asked by their users. Although CQA frameworks receive new questions in a linear rate, the rate of the unanswered questions increases in an exponential way. This variation eventually compromise effectiveness of the CQA frameworks as knowledge sharing platforms. The main cause for this challenge is the improper routing of questions to the potential answerers, field experts or interested users. The proposed technique QR-DSSM uses deep semantic similarity model (DSSM) to extract semantic similarity features using deep neural networks. The extracted semantic features are used to rank the profiles of the answerers by their relevance the routed question. QR-DSSM maps the asked questions and the profiles of the users into a latent semantic space where the relevance is measured using cosine similarity between the two; questions and users’ profiles. QR-DSSM achieved MRR score of 0.1737. QR-DSSM outperformed the baseline models such as query likelihood language model (QLLM), Latent Dirichlet Allocation (LDA), SVM classification technique and RankingSVM learning to rank technique.
- Subjects :
- Information retrieval
Computer science
business.industry
Deep learning
InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL
Cosine similarity
02 engineering and technology
Latent Dirichlet allocation
symbols.namesake
Semantic similarity
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
symbols
Question answering
020201 artificial intelligence & image processing
Relevance (information retrieval)
Learning to rank
Language model
Artificial intelligence
business
Subjects
Details
- ISBN :
- 978-3-319-55752-6
- ISBNs :
- 9783319557526
- Database :
- OpenAIRE
- Journal :
- Database Systems for Advanced Applications ISBN: 9783319557526, DASFAA (1)
- Accession number :
- edsair.doi...........f8ceb6bd81aadb681d62a40127a9ca5a