TY - GEN
T1 - Simultaneous Contextualization and Interpretation with Keyword Awareness
AU - Yoshino, Teppei
AU - Matsumori, Shoya
AU - Fukuchi, Yosuke
AU - Imai, Michita
N1 - Funding Information:
This work was supported by JST CREST Grant Number JPMJCR19A1, Japan.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Most natural-language-processing methods are designed for estimating context given an entire set of sentences at once. However, dialogue is incremental in nature. SCAIN (Simultaneous Contextualization and Interpretation) is an algorithm for incremental dialogue processing. Along with the progress of the dialogue, it can solve the interdependence problem in which the interpretation of words depends on the context, and the context is determined by the interpreted words. However, SCAIN cannot process texts that contain more words insignificant to context estimation such as in longer texts. We propose SCAIN with keyword extraction (SCAIN/KE), which extracts keywords that contribute to context estimation and eliminates the effect of insignificant words so that it can process longer texts. In the case study, SCAIN/KE updates context and interpretation better than SCAIN and obtains the keywords that contribute to context estimation better than other statistical methods. In the experiments, we evaluated SCAIN/KE on solving the ambiguity of polysemous words using the Wikipedia disambiguation pages. The results indicate that SCAIN/KE is more accurate than SCAIN.
AB - Most natural-language-processing methods are designed for estimating context given an entire set of sentences at once. However, dialogue is incremental in nature. SCAIN (Simultaneous Contextualization and Interpretation) is an algorithm for incremental dialogue processing. Along with the progress of the dialogue, it can solve the interdependence problem in which the interpretation of words depends on the context, and the context is determined by the interpreted words. However, SCAIN cannot process texts that contain more words insignificant to context estimation such as in longer texts. We propose SCAIN with keyword extraction (SCAIN/KE), which extracts keywords that contribute to context estimation and eliminates the effect of insignificant words so that it can process longer texts. In the case study, SCAIN/KE updates context and interpretation better than SCAIN and obtains the keywords that contribute to context estimation better than other statistical methods. In the experiments, we evaluated SCAIN/KE on solving the ambiguity of polysemous words using the Wikipedia disambiguation pages. The results indicate that SCAIN/KE is more accurate than SCAIN.
KW - Dialogue context
KW - Keyword extraction
KW - Polysemy
KW - SCAIN
KW - SLAM
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U2 - 10.1007/978-3-030-87897-9_36
DO - 10.1007/978-3-030-87897-9_36
M3 - Conference contribution
AN - SCOPUS:85117692494
SN - 9783030878962
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 403
EP - 413
BT - Artificial Intelligence and Soft Computing - 20th International Conference, ICAISC 2021, Proceedings
A2 - Rutkowski, Leszek
A2 - Scherer, Rafał
A2 - Korytkowski, Marcin
A2 - Pedrycz, Witold
A2 - Tadeusiewicz, Ryszard
A2 - Zurada, Jacek M.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2021
Y2 - 21 June 2021 through 23 June 2021
ER -