TY - GEN
T1 - Regarding the Semantics Combined with N-gram between Question and Answer Statements to Obtain Factor Score
AU - Yokoyama, Yuya
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - For the purpose of resolving the issues of mismatches between the intentions of questioners and respondents at Question and Answer (Q&A) sites, nine factors of impressions for Q&A statements were experimentally obtained through factor analysis. Factor scores were then estimated by using the feature values of statements through multiple regression analysis. The factor scores estimated were subsequently used for finding respondents who would be expected to give appropriate answers to a posted question. Then, in order to consider the semantics of the Q&A statement, word2vecs were appended to the feature values of statements to estimate factor scores. It was demonstrated that word2vec showed the potential of improving estimation accuracy, regardless of the feature values based on 2-gram or syntactic information. For the method using N -gram, it would be necessary to analyze whether longer units than 2-gram could be applicable to this methodology. Thus, this paper aims to append N-gram beyond 3-gram to word2vec and closing sentence expressions as the feature values of statements. The analysis result, taking cross-validation into consideration, has shown that word2vec, as well as units of either 3-gram, 4-gram, or 5-gram, would be applicable to estimating factor scores of Q&A statements.
AB - For the purpose of resolving the issues of mismatches between the intentions of questioners and respondents at Question and Answer (Q&A) sites, nine factors of impressions for Q&A statements were experimentally obtained through factor analysis. Factor scores were then estimated by using the feature values of statements through multiple regression analysis. The factor scores estimated were subsequently used for finding respondents who would be expected to give appropriate answers to a posted question. Then, in order to consider the semantics of the Q&A statement, word2vecs were appended to the feature values of statements to estimate factor scores. It was demonstrated that word2vec showed the potential of improving estimation accuracy, regardless of the feature values based on 2-gram or syntactic information. For the method using N -gram, it would be necessary to analyze whether longer units than 2-gram could be applicable to this methodology. Thus, this paper aims to append N-gram beyond 3-gram to word2vec and closing sentence expressions as the feature values of statements. The analysis result, taking cross-validation into consideration, has shown that word2vec, as well as units of either 3-gram, 4-gram, or 5-gram, would be applicable to estimating factor scores of Q&A statements.
KW - Factor Score
KW - Multiple Regression Analysis
KW - N-gram
KW - Q&A Site
KW - word2vec
UR - https://www.scopus.com/pages/publications/85211320360
UR - https://www.scopus.com/pages/publications/85211320360#tab=citedBy
U2 - 10.1109/BCD61269.2024.10743094
DO - 10.1109/BCD61269.2024.10743094
M3 - Conference contribution
AN - SCOPUS:85211320360
T3 - 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024
SP - 115
EP - 120
BT - 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024
A2 - Shiramatu, Shun
A2 - Okuhara, Shun
A2 - Wen, Gu
A2 - Haqbeen, Jawad
A2 - Iwashita, Motoi
A2 - Shimoda, Atsushi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2024
Y2 - 16 July 2024 through 18 July 2024
ER -