Question Classification for Intelligent Question Answering: A Comprehensive Survey
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/12/10/415 |
| _version_ | 1827761570425339904 |
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| author | Hao Sun Shu Wang Yunqiang Zhu Wen Yuan Zhiqiang Zou |
| author_facet | Hao Sun Shu Wang Yunqiang Zhu Wen Yuan Zhiqiang Zou |
| author_sort | Hao Sun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T10:13:49Z |
| format | Article |
| id | doaj.art-709327af82f54ad0a31ac9c0f7186f74 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-11T10:13:49Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-709327af82f54ad0a31ac9c0f7186f742023-11-16T10:30:37ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-10-01121041510.3390/ijgi12100415Question Classification for Intelligent Question Answering: A Comprehensive SurveyHao Sun0Shu Wang1Yunqiang Zhu2Wen Yuan3Zhiqiang Zou4College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaInstitute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaCollege of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaIn 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.https://www.mdpi.com/2220-9964/12/10/415Intelligent Question Answering (IQA)GeoAIQuestion Classification (QC)IQA_QC frameworkevaluation metricsliterature review |
| spellingShingle | Hao Sun Shu Wang Yunqiang Zhu Wen Yuan Zhiqiang Zou Question Classification for Intelligent Question Answering: A Comprehensive Survey ISPRS International Journal of Geo-Information Intelligent Question Answering (IQA) GeoAI Question Classification (QC) IQA_QC framework evaluation metrics literature review |
| title | Question Classification for Intelligent Question Answering: A Comprehensive Survey |
| title_full | Question Classification for Intelligent Question Answering: A Comprehensive Survey |
| title_fullStr | Question Classification for Intelligent Question Answering: A Comprehensive Survey |
| title_full_unstemmed | Question Classification for Intelligent Question Answering: A Comprehensive Survey |
| title_short | Question Classification for Intelligent Question Answering: A Comprehensive Survey |
| title_sort | question classification for intelligent question answering a comprehensive survey |
| topic | Intelligent Question Answering (IQA) GeoAI Question Classification (QC) IQA_QC framework evaluation metrics literature review |
| url | https://www.mdpi.com/2220-9964/12/10/415 |
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