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Identifying the Focus Word in Natural Language Questions Based on Association Rules.

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Title: Identifying the Focus Word in Natural Language Questions Based on Association Rules.
Authors: Hu, Xin1 (AUTHOR), Ren, Xiaofeng1 (AUTHOR), Zheng, Jian2 (AUTHOR), Duan, Jiangli1 (AUTHOR) duanjl@yznu.edu.cn, Zhang, Sulan1 (AUTHOR), Murray, Richard (AUTHOR) rmurray@wiley.com
Source: International Journal of Intelligent Systems. 5/9/2026, Vol. 2026, p1-13. 13p.
Subjects: Association rule mining, Indexes, Natural language processing, Question answering systems
Abstract: Knowledge base‐based intelligent question‐answering systems have insufficient understanding of the questions. In the early stages of research, it is effective in most cases that the existing natural language question‐understanding methods can answer questions by connecting entities and relationships when ignoring the identification of focus words. However, as research deepens, ignoring focus words has become a shortcoming. To address this, we propose identifying focus words, enabling more precise understanding of user focus. We define focus itemset, frequent focus itemset, focus association rule, and strong focus association rule to express focus‐related information better. Given the unique nature of focus association rules, we propose a prefix tree structure and an algorithm for mining association rules aimed at identifying focus words. We also introduce an inverted index specifically designed for focus association rules and propose an efficient algorithm for identifying focus words based on this index. Experiments verify the effectiveness of our algorithm and the efficiency of the inverted index, with a focus word identification rate exceeding 90%. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Identifying the Focus Word in Natural Language Questions Based on Association Rules.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Intelligent+Systems%22">International Journal of Intelligent Systems</searchLink>. 5/9/2026, Vol. 2026, p1-13. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Association+rule+mining%22">Association rule mining</searchLink><br /><searchLink fieldCode="DE" term="%22Indexes%22">Indexes</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Question+answering+systems%22">Question answering systems</searchLink>
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  Data: Knowledge base‐based intelligent question‐answering systems have insufficient understanding of the questions. In the early stages of research, it is effective in most cases that the existing natural language question‐understanding methods can answer questions by connecting entities and relationships when ignoring the identification of focus words. However, as research deepens, ignoring focus words has become a shortcoming. To address this, we propose identifying focus words, enabling more precise understanding of user focus. We define focus itemset, frequent focus itemset, focus association rule, and strong focus association rule to express focus‐related information better. Given the unique nature of focus association rules, we propose a prefix tree structure and an algorithm for mining association rules aimed at identifying focus words. We also introduce an inverted index specifically designed for focus association rules and propose an efficient algorithm for identifying focus words based on this index. Experiments verify the effectiveness of our algorithm and the efficiency of the inverted index, with a focus word identification rate exceeding 90%. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Intelligent Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1155/int/4126368
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      – Code: eng
        Text: English
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      – SubjectFull: Natural language processing
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      – SubjectFull: Question answering systems
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      – TitleFull: Identifying the Focus Word in Natural Language Questions Based on Association Rules.
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              Text: 5/9/2026
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              Y: 2026
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