| Presentation | 2022-05-27 Improvement of Performance of Question and Answering System using Ontology Generation Ayato Kuwana, Incheon Paik, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Automating ontology generation from raw text corpus is required to meet the ontology demand. As an initial attempt of ontology generation with a neural network, a recurrent neural network (RNN)-based method is proposed. However, updating the architecture is possible because of the development in natural language processing (NLP). In contrast, the transfer learning of language models trained by a large unlabeled corpus such as bidirectional encoder representations from transformers (BERT) has yielded a breakthrough in NLP. Inspired by these achievements, to apply transfer learning of language models, we propose a novel workflow for ontology generation consisting of two-stage learning. This paper provides a quantitative comparison between the proposed method and the existing methods. Our result showed that our best method improved accuracy by over 12.5%. To show an application example, we applied our model to Stanford Question Answering Dataset (SQuAD) dataset to show ontology generation in a real field. The result shows our model can generate good ontology with some exceptions that requests future research for improving the ontology quality. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | OntologyAutomation of GenerationDeep Pretrained ModelQuestion and Answering System |
| Paper # | SC2022-7 |
| Date of Issue | 2022-05-20 (SC) |
| Conference Information | |
| Committee | SC |
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| Conference Date | 2022/5/27(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | AI Service and Digital Transformation, and general topics |
| Chair | Shinji Kikuchi(NIMS) |
| Vice Chair | Yoji Yamato(NTT) / Kosaku Kimura(Fujitsu) |
| Secretary | Yoji Yamato(Kobe Univ.) / Kosaku Kimura(Tokyo Univ. of Tech.) |
| Assistant | Shin Tezuka(Hitachi) / Takao Nakaguchi(KCGI) |
| Paper Information | |
| Registration To | Technical Committee on Service Computing |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Improvement of Performance of Question and Answering System using Ontology Generation |
| Sub Title (in English) | |
| Keyword(1) | OntologyAutomation of GenerationDeep Pretrained ModelQuestion and Answering System |
| 1st Author's Name | Ayato Kuwana |
| 1st Author's Affiliation | University of Aizu(UoA) |
| 2nd Author's Name | Incheon Paik |
| 2nd Author's Affiliation | University of Aizu(UoA) |
| Date | 2022-05-27 |
| Paper # | SC2022-7 |
| Volume (vol) | vol.122 |
| Number (no) | SC-50 |
| Page | pp.pp.37-42(SC), |
| #Pages | 6 |
| Date of Issue | 2022-05-20 (SC) |