Template-based Question Answering using Recursive Neural Networks

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Standard

Template-based Question Answering using Recursive Neural Networks. / Athreya, Ram G.; Bansal, Srividya K.; Ngomo, Axel Cyrille Ngonga et al.
Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 195-198 9364639 (Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021).

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Harvard

Athreya, RG, Bansal, SK, Ngomo, ACN & Usbeck, R 2021, Template-based Question Answering using Recursive Neural Networks. in Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021., 9364639, Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 195-198, 15th IEEE International Conference on Semantic Computing - ICSC 2021, Virtual, Laguna Hills, California, United States, 27.01.21. https://doi.org/10.48550/arXiv.2004.13843, https://doi.org/10.1109/ICSC50631.2021.00041

APA

Athreya, R. G., Bansal, S. K., Ngomo, A. C. N., & Usbeck, R. (2021). Template-based Question Answering using Recursive Neural Networks. In Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021 (pp. 195-198). Article 9364639 (Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.48550/arXiv.2004.13843, https://doi.org/10.1109/ICSC50631.2021.00041

Vancouver

Athreya RG, Bansal SK, Ngomo ACN, Usbeck R. Template-based Question Answering using Recursive Neural Networks. In Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 195-198. 9364639. (Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021). doi: 10.48550/arXiv.2004.13843, 10.1109/ICSC50631.2021.00041

Bibtex

@inbook{f10ca7e14624471a87cdd54163274ed6,
title = "Template-based Question Answering using Recursive Neural Networks",
abstract = "Most question answering (QA) systems over Linked Data, i.e. Knowledge Graphs, approach the question answering task as a conversion from a natural language question to its corresponding SPARQL query. A common approach is to use query templates to generate SPARQL queries with slots that need to be filled. Using templates instead of running an extensive NLP pipeline or end-to-end model shifts the QA problem into a classification task, where the system needs to match the input question to the appropriate template. This paper presents an approach to automatically learn and classify natural language questions into corresponding templates using recursive neural networks. Our model was trained on 5000 questions and their respective SPARQL queries from the preexisting LC-QuAD dataset grounded in DBpedia, spanning 5042 entities and 615 predicates. The resulting model was evaluated using the FAIR GERBIL QA framework resulting in 0.419 macro f-measure on LC-QuAD and 0.417 macro f-measure on QALD-7.",
keywords = "Question Answering, Recursive Neural Network, Informatics, Business informatics",
author = "Athreya, {Ram G.} and Bansal, {Srividya K.} and Ngomo, {Axel Cyrille Ngonga} and Ricardo Usbeck",
note = "Funding Information: This paper presents a novel approach for the QA over Linked Data task by converting it into a template classification task followed by a slot filling task. Although earlier template-based approaches have attempted similar solutions, this was the first time (to the best of our knowledge) that recursive neural networks were applied to the template classification task. For completeness, a slot filling approach using an ensemble of the best components for named entity, predicate and class recognition tasks were presented. Our evaluation showed that state-of-the-art neural network techniques such as Long Short Term Memory (LSTM), recursive neural networks, and word embeddings be leveraged for the template classification task. We are aware that our approach has a coverage issue in terms of being bound to the training templates and look forward to mitigating this issue through a finer-grained training process. Acknowledgements. We acknowledge the support of the Federal Ministry for Economic Affairs and Energy (BMWi) project SPEAKER (FKZ 01MK20011A). Publisher Copyright: {\textcopyright} 2021 IEEE.; 15th IEEE International Conference on Semantic Computing - ICSC 2021, ICSC 2021 ; Conference date: 27-01-2021 Through 29-01-2021",
year = "2021",
month = jan,
doi = "10.48550/arXiv.2004.13843",
language = "English",
isbn = "978-1-7281-8900-0",
series = "Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "195--198",
booktitle = "Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021",
address = "United States",

}

RIS

TY - CHAP

T1 - Template-based Question Answering using Recursive Neural Networks

AU - Athreya, Ram G.

AU - Bansal, Srividya K.

AU - Ngomo, Axel Cyrille Ngonga

AU - Usbeck, Ricardo

N1 - Conference code: 15

PY - 2021/1

Y1 - 2021/1

N2 - Most question answering (QA) systems over Linked Data, i.e. Knowledge Graphs, approach the question answering task as a conversion from a natural language question to its corresponding SPARQL query. A common approach is to use query templates to generate SPARQL queries with slots that need to be filled. Using templates instead of running an extensive NLP pipeline or end-to-end model shifts the QA problem into a classification task, where the system needs to match the input question to the appropriate template. This paper presents an approach to automatically learn and classify natural language questions into corresponding templates using recursive neural networks. Our model was trained on 5000 questions and their respective SPARQL queries from the preexisting LC-QuAD dataset grounded in DBpedia, spanning 5042 entities and 615 predicates. The resulting model was evaluated using the FAIR GERBIL QA framework resulting in 0.419 macro f-measure on LC-QuAD and 0.417 macro f-measure on QALD-7.

AB - Most question answering (QA) systems over Linked Data, i.e. Knowledge Graphs, approach the question answering task as a conversion from a natural language question to its corresponding SPARQL query. A common approach is to use query templates to generate SPARQL queries with slots that need to be filled. Using templates instead of running an extensive NLP pipeline or end-to-end model shifts the QA problem into a classification task, where the system needs to match the input question to the appropriate template. This paper presents an approach to automatically learn and classify natural language questions into corresponding templates using recursive neural networks. Our model was trained on 5000 questions and their respective SPARQL queries from the preexisting LC-QuAD dataset grounded in DBpedia, spanning 5042 entities and 615 predicates. The resulting model was evaluated using the FAIR GERBIL QA framework resulting in 0.419 macro f-measure on LC-QuAD and 0.417 macro f-measure on QALD-7.

KW - Question Answering

KW - Recursive Neural Network

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85102617112&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/930ff2cd-68eb-3440-9c15-60501e8bba0a/

U2 - 10.48550/arXiv.2004.13843

DO - 10.48550/arXiv.2004.13843

M3 - Article in conference proceedings

AN - SCOPUS:85102617112

SN - 978-1-7281-8900-0

T3 - Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021

SP - 195

EP - 198

BT - Proceedings - 2021 IEEE 15th International Conference on Semantic Computing, ICSC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th IEEE International Conference on Semantic Computing - ICSC 2021

Y2 - 27 January 2021 through 29 January 2021

ER -

Recently viewed

Publications

  1. Exploiting linear partial information for optimal use of forecasts. With an application to U.S. economic policy
  2. From entity to process
  3. A MODEL FOR QUANTIFICATION OF SOFTWARE COMPLEXITY
  4. Model predictive control for switching gain adaptation in a sliding mode controller of a DC drive with nonlinear friction
  5. A Control Scheme for PMSMs using Model Predictive Control and a Feedforward Action in the Presence of Saturated Inputs
  6. Promising practices for dealing with complexity in research for development
  7. Sliding-Mode-Based Input-Output Linearization of a Peltier Element for Ice Clamping Using a State and Disturbance Observer
  8. Energy Optimization in Motion Planning of a Two-Link Manipulator using Bernstein Polynomials
  9. Children's use of spatial skills in solving two map-reading tasks in real space.
  10. Topic Embeddings – A New Approach to Classify Very Short Documents Based on Predefined Topics
  11. A tutorial introduction to adaptive fractal analysis
  12. Template-based Question Answering using Recursive Neural Networks
  13. A sensor fault detection scheme as a functional safety feature for DC-DC converters
  14. Evaluating structural and compositional canopy characteristics to predict the light-demand signature of the forest understorey in mixed, semi-natural temperate forests
  15. lp-Norm Multiple Kernel Learning
  16. Design optimization of spiral coils for textile applications by genetic algorithm
  17. Exact and approximate inference for annotating graphs with structural SVMs
  18. Fast, Fully Automated Analysis of Voriconazole from Serum by LC-LC-ESI-MS-MS with Parallel Column-Switching Technique
  19. Recurrence Quantification Analysis of Processes and Products of Discourse
  20. Lessons learned for spatial modelling of ecosystem services in support of ecosystem accounting
  21. Construct Objectification and De-Objectification in Organization Theory
  22. Computational modeling of amorphous polymers
  23. Modeling and numerical simulation of multiscale behavior in polycrystals via extended crystal plasticity
  24. Influence of Process Parameters and Die Design on the Microstructure and Texture Development of Direct Extruded Magnesium Flat Products
  25. Simple saturated PID control for fast transient of motion systems
  26. Dynamic Lot Size Optimization with Reinforcement Learning
  27. The delay vector variance method and the recurrence quantification analysis of energy markets
  28. Introducing parametric uncertainty into a nonlinear friction model
  29. Faulty Process Detection Using Machine Learning Techniques
  30. TextGraphs 2024 Shared Task on Text-Graph Representations for Knowledge Graph Question Answering
  31. Clause identification using entropy guided transformation learning
  32. Mathematical Modeling for Robot 3D Laser Scanning in Complete Darkness Environments to Advance Pipeline Inspection
  33. Dispatching rule selection with Gaussian processes
  34. Constraints are the solution, not the problem
  35. Dynamic priority based dispatching of AGVs in flexible job shops