TY - GEN
T1 - Application of 5-gram to Obtain Factor Scores of Japanese Q&A Statements
AU - Yokoyama, Yuya
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In order to clarify the issue of mismatches between the questioners and respondents of Question and Answer (Q&A) sites, nine factors of impressions for Q&A statements were obtained through impression evaluation experiments. Then through multiple regression analysis, factor scores were estimated with the usage of feature values of statements. The factor scores estimated and obtained were subsequently utilized for detecting respondents who would be expected to appropriately answer a posted question. Nevertheless, this method has considerably been dependent on the syntactic information extracted through morphological analysis. Moreover, this method has a vital downside of requiring numerous explanatory variables and complicated multiple regression equation to estimate factor scores. Therefore, the principle has been shifted to applying N-gram over morphological analysis. In this paper, 5-gram is applied to the feature values, in a similar way as the previous analysis using shorter N-grams. Further analysis has shown that 5-gram would be also applicable to the method. In terms of estimation accuracy, N-grams also outscore morphological analysis; above all 2-gram and 3-gram show the best accuracy. Hence, it could be suggested that N-gram should play more important role in estimating factor scores than mere morphological analysis.
AB - In order to clarify the issue of mismatches between the questioners and respondents of Question and Answer (Q&A) sites, nine factors of impressions for Q&A statements were obtained through impression evaluation experiments. Then through multiple regression analysis, factor scores were estimated with the usage of feature values of statements. The factor scores estimated and obtained were subsequently utilized for detecting respondents who would be expected to appropriately answer a posted question. Nevertheless, this method has considerably been dependent on the syntactic information extracted through morphological analysis. Moreover, this method has a vital downside of requiring numerous explanatory variables and complicated multiple regression equation to estimate factor scores. Therefore, the principle has been shifted to applying N-gram over morphological analysis. In this paper, 5-gram is applied to the feature values, in a similar way as the previous analysis using shorter N-grams. Further analysis has shown that 5-gram would be also applicable to the method. In terms of estimation accuracy, N-grams also outscore morphological analysis; above all 2-gram and 3-gram show the best accuracy. Hence, it could be suggested that N-gram should play more important role in estimating factor scores than mere morphological analysis.
KW - Factor Score
KW - Multiple Regression Analysis
KW - N-gram
KW - Q&A Site
UR - https://www.scopus.com/pages/publications/85176932212
UR - https://www.scopus.com/pages/publications/85176932212#tab=citedBy
U2 - 10.1109/IIAI-AAI59060.2023.00024
DO - 10.1109/IIAI-AAI59060.2023.00024
M3 - Conference contribution
AN - SCOPUS:85176932212
T3 - Proceedings - 2023 14th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2023
SP - 69
EP - 75
BT - Proceedings - 2023 14th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2023
Y2 - 8 July 2023 through 13 July 2023
ER -