An Overview of Utilizing Knowledge Bases in Neural Networks for Question Answering
| Autor: | Sabin Kafle, Dejing Dou, Nisansa de Silva |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer Networks and Communications
Computer science Information access Context (language use) 02 engineering and technology 010501 environmental sciences 01 natural sciences Theoretical Computer Science Knowledge-based systems Knowledge extraction 020204 information systems Component (UML) 0502 economics and business 0202 electrical engineering electronic engineering information engineering Question answering 0105 earth and related environmental sciences Information retrieval Artificial neural network business.industry 05 social sciences Identification (information) Knowledge base 050211 marketing business Software Natural language Information Systems |
| Zdroj: | IRI |
| ISSN: | 1572-9419 1387-3326 |
| DOI: | 10.1007/s10796-020-10035-2 |
| Popis: | Question Answering (QA) requires understanding of queries expressed in natural languages and identification of relevant information content to provide an answer. For closed-world QAs, information access is obtained by means of either context texts, or a Knowledge Base (KB), or both. KBs are human-generated schematic representations of world knowledge. The representational ability of neural networks to generalize world information makes it an important component of current QA research. In this paper, we study the neural networks and QA systems in the context of KBs. Specifically, we focus on surveying methods for KB embedding, how such embeddings are integrated into the neural networks, and the role such embeddings play in improving performance across different question-answering problems. Our study of multiple question answering methods finds that the neural networks are able to produce state-of-art results in different question answering domains, and inclusion of additional information via KB embeddings further improve the performance of such approaches. Further progress in QA can be improved by incorporating more powerful representations of KBs. |
| Databáze: | OpenAIRE |
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