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@inproceedings{1473521,
author = {Novotný, Vít and Sojka, Petr},
address = {Brno},
booktitle = {Proceedings of the Twelfth Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2018},
editor = {Aleš Horák, Pavel Rychlý, Adam Rambousek},
keywords = {passage retrieval; question answering; Godwin’s law; SemEval; weighting of document passages},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Brno},
isbn = {978-80-263-1517-9},
pages = {31-40},
publisher = {Tribun EU},
title = {Weighting of Passages in Question Answering},
url = {http://raslan2018.nlp-consulting.net/},
year = {2018}
}
TY - JOUR
ID - 1473521
AU - Novotný, Vít - Sojka, Petr
PY - 2018
TI - Weighting of Passages in Question Answering
PB - Tribun EU
CY - Brno
SN - 9788026315179
KW - passage retrieval
KW - question answering
KW - Godwin’s law
KW - SemEval
KW - weighting of document passages
UR - http://raslan2018.nlp-consulting.net/
N2 - Modern text retrieval systems employ text segmentation during the indexing of documents. We show that, rather than returning the passages to the user, significant improvements are achieved on the semantic text similarity task on question answering (QA) datasets by combining all passages from a document into a single result with an aggregate similarity score. Following an analysis of the SemEval-2016 and 2017 task 3 datasets, we develop a weighted averaging operator that achieves state-of-the-art results on subtask B and can be implemented into existing search engines. Segmentation in information retrieval matters. Our results show that paying attention to important passages by using a task-specific weighting method leads to the best results on these question answering domain retrieval tasks.
ER -
NOVOTNÝ, Vít a Petr SOJKA. Weighting of Passages in Question Answering. In Aleš Horák, Pavel Rychlý, Adam Rambousek. \textit{Proceedings of the Twelfth Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2018}. Brno: Tribun EU, 2018, s.~31-40. ISBN~978-80-263-1517-9.
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