Question Classification for Intelligent Question Answering: A Comprehensive Survey
文献类型：期刊论文
| 作者 | Sun, Hao; Wang, Shu4; Zhu, Yunqiang3,4; Yuan, Wen4; Zou, Zhiqiang2 |
| 刊名 | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
 |
| 出版日期 | 2023-10-01 |
| 卷号 | 12期号:10页码:415 |
| 关键词 | Intelligent Question Answering (IQA) GeoAI Question Classification (QC) IQA_QC framework evaluation metrics literature review |
| DOI | 10.3390/ijgi12100415 |
| 产权排序 | 2 |
| 文献子类 | Review |
| 英文摘要 | In the era of GeoAI, Geospatial Intelligent Question Answering (GeoIQA) represents the ultimate pursuit for everyone. Even generative AI systems like ChatGPT-4 struggle to handle complex GeoIQA. GeoIQA is domain complex IQA, which aims at understanding and answering questions accurately. The core of IQA is the Question Classification (QC), which mainly contains four types: content-based, template-based, calculation-based and method-based classification. These IQA_QC frameworks, however, struggle to be compatible and integrate with each other, which may be the bottleneck restricting the substantial improvement of IQA performance. To address this problem, this paper reviewed recent advances on IQA with the focus on solving question classification and proposed a comprehensive IQA_QC framework for understanding user query intention more accurately. By introducing the basic idea of the IQA mechanism, a three-level question classification framework consisting of essence, form and implementation is put forward which could cover the complexity and diversity of geographical questions. In addition, the proposed IQA_QC framework revealed that there are still significant deficiencies in the IQA evaluation metrics in the aspect of broader dimensions, which led to low answer performance, functional performance and systematic performance. Through the comparisons, we find that the proposed IQA_QC framework can fully integrate and surpass the existing classification. Although our proposed classification can be further expanded and improved, we firmly believe that this comprehensive IQA_QC framework can effectively help researchers in both semantic parsing and question querying processes. Furthermore, the IQA_QC framework can also provide a systematic question-and-answer pair/library categorization system for AIGCs, such as GPT-4. In conclusion, whether it is explicit GeoAI or implicit GeoAI, the IQA_QC can play a pioneering role in providing question-and-answer types in the future. |
| WOS研究方向 | Computer Science ; Physical Geography ; Remote Sensing |
| WOS记录号 | WOS:001093589300001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/199467]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wang, Shu |
| 作者单位 | 1.Nanjing Univ Posts & Telecommun, Jiangsu Key Lab Big Data Secur & Intelligent Proc, Nanjing 210023, Peoples R China 2.Jiangsu Ctr Collaborat Innovat Geog Informat, Nanjing 210023, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Nanjing Univ Posts & Telecommun, Coll Comp, Nanjing 210023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Sun, Hao,Wang, Shu,Zhu, Yunqiang,et al. Question Classification for Intelligent Question Answering: A Comprehensive Survey[J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,2023,12(10):415. |
| APA | Sun, Hao,Wang, Shu,Zhu, Yunqiang,Yuan, Wen,&Zou, Zhiqiang.(2023).Question Classification for Intelligent Question Answering: A Comprehensive Survey.ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION,12(10),415. |
| MLA | Sun, Hao,et al."Question Classification for Intelligent Question Answering: A Comprehensive Survey".ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 12.10(2023):415. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。