Summarization of Yes/No Questions Using a Feature Function Model

Jing He, Decheng Dai
Proceedings of the Asian Conference on Machine Learning, PMLR 20:351-366, 2011.

Abstract

Answer summarization is an important problem in the study of Question and Answering. In this paper, we deal with the general questions with “Yes/No” answers in English. We design 1) a model to score the relevance of the answers and the questions, and 2) a feature function combining the relevance and opinion scores to classify each answer to be “Yes”, “No” or “Neutral”. We combine the opinion features together with two weighting scores to solve this problem and conduct experiments on a real word dataset. Given an input question, the system firstly detects if it can be simply answered by “Yes/No” or not, and then outputs the resulting voting numbers of “Yes” answers and “No” answers to this question. We also first proposed the accuracy, precision, and recall to the “Yes/No” answer detection.

Cite this Paper

BibTeX
@InProceedings{pmlr-v20-he11, title = {Summarization of Yes/No Questions Using a Feature Function Model}, author = {He, Jing and Dai, Decheng}, booktitle = {Proceedings of the Asian Conference on Machine Learning}, pages = {351--366}, year = {2011}, editor = {Hsu, Chun-Nan and Lee, Wee Sun}, volume = {20}, series = {Proceedings of Machine Learning Research}, address = {South Garden Hotels and Resorts, Taoyuan, Taiwain}, month = {14--15 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v20/he11/he11.pdf}, url = {https://proceedings.mlr.press/v20/he11.html}, abstract = {Answer summarization is an important problem in the study of Question and Answering. In this paper, we deal with the general questions with “Yes/No” answers in English. We design 1) a model to score the relevance of the answers and the questions, and 2) a feature function combining the relevance and opinion scores to classify each answer to be “Yes”, “No” or “Neutral”. We combine the opinion features together with two weighting scores to solve this problem and conduct experiments on a real word dataset. Given an input question, the system firstly detects if it can be simply answered by “Yes/No” or not, and then outputs the resulting voting numbers of “Yes” answers and “No” answers to this question. We also first proposed the accuracy, precision, and recall to the “Yes/No” answer detection.} }
Endnote
%0 Conference Paper %T Summarization of Yes/No Questions Using a Feature Function Model %A Jing He %A Decheng Dai %B Proceedings of the Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2011 %E Chun-Nan Hsu %E Wee Sun Lee %F pmlr-v20-he11 %I PMLR %P 351--366 %U https://proceedings.mlr.press/v20/he11.html %V 20 %X Answer summarization is an important problem in the study of Question and Answering. In this paper, we deal with the general questions with “Yes/No” answers in English. We design 1) a model to score the relevance of the answers and the questions, and 2) a feature function combining the relevance and opinion scores to classify each answer to be “Yes”, “No” or “Neutral”. We combine the opinion features together with two weighting scores to solve this problem and conduct experiments on a real word dataset. Given an input question, the system firstly detects if it can be simply answered by “Yes/No” or not, and then outputs the resulting voting numbers of “Yes” answers and “No” answers to this question. We also first proposed the accuracy, precision, and recall to the “Yes/No” answer detection.
RIS
TY - CPAPER TI - Summarization of Yes/No Questions Using a Feature Function Model AU - Jing He AU - Decheng Dai BT - Proceedings of the Asian Conference on Machine Learning DA - 2011/11/17 ED - Chun-Nan Hsu ED - Wee Sun Lee ID - pmlr-v20-he11 PB - PMLR DP - Proceedings of Machine Learning Research VL - 20 SP - 351 EP - 366 L1 - http://proceedings.mlr.press/v20/he11/he11.pdf UR - https://proceedings.mlr.press/v20/he11.html AB - Answer summarization is an important problem in the study of Question and Answering. In this paper, we deal with the general questions with “Yes/No” answers in English. We design 1) a model to score the relevance of the answers and the questions, and 2) a feature function combining the relevance and opinion scores to classify each answer to be “Yes”, “No” or “Neutral”. We combine the opinion features together with two weighting scores to solve this problem and conduct experiments on a real word dataset. Given an input question, the system firstly detects if it can be simply answered by “Yes/No” or not, and then outputs the resulting voting numbers of “Yes” answers and “No” answers to this question. We also first proposed the accuracy, precision, and recall to the “Yes/No” answer detection. ER -
APA
He, J. & Dai, D.. (2011). Summarization of Yes/No Questions Using a Feature Function Model. Proceedings of the Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 20:351-366 Available from https://proceedings.mlr.press/v20/he11.html.

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