Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/101818
Title: Comparative web search questions
Authors: Bondarenko, A.
Braslavski, P.
Völske, M.
Aly, R.
Fröbe, M.
Panchenko, A.
Biemann, C.
Stein, B.
Hagen, M.
Issue Date: 2020
Publisher: Association for Computing Machinery, Inc
Citation: Comparative web search questions / A. Bondarenko, P. Braslavski, M. Völske, et al. — DOI 10.1145/3336191.3371848 // WSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining. — 2020. — P. 52-60.
Abstract: We analyze comparative questions, i.e., questions asking to compare different items, that were submitted to Yandex in 2012. Responses to such questions might be quite different from the simple “ten blue links” and could, for example, aggregate pros and cons of the different options as direct answers. However, changing the result presentation is an intricate decision such that the classification of comparative questions forms a highly precision-oriented task. From a year-long Yandex log, we annotate a random sample of 50,000 questions; 2.8% of which are comparative. For these annotated questions, we develop a precision-oriented classifier by combining carefully hand-crafted lexico-syntactic rules with feature-based and neural approaches—achieving a recall of 0.6 at a perfect precision of 1.0. After running the classifier on the full year log (on average, there is at least one comparative question per second), we analyze 6,250 comparative questions using more fine-grained subclasses (e.g., should the answer be a “simple” fact or rather a more verbose argument) for which individual classifiers are trained. An important insight is that more than 65% of the comparative questions demand argumentation and opinions, i.e., reliable direct answers to comparative questions require more than the facts from a search engine’s knowledge graph. In addition, we present a qualitative analysis of the underlying comparative information needs (separated into 14 categories like consumer electronics or health), their seasonal dynamics, and possible answers from community question answering platforms. © 2020 Copyright held by the owner/author(s).
Keywords: QUERY LOG ANALYSIS
QUESTION ANSWERING
QUESTION CLASSIFICATION
INFORMATION RETRIEVAL
SEARCH ENGINES
SYNTACTICS
WEBSITES
COMMUNITY QUESTION ANSWERING
INDIVIDUAL CLASSIFIERS
KNOWLEDGE GRAPHS
QUALITATIVE ANALYSIS
QUERY LOG ANALYSIS
QUESTION ANSWERING
QUESTION CLASSIFICATION
SEASONAL DYNAMICS
DATA MINING
URI: http://hdl.handle.net/10995/101818
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85079535949
PURE ID: 12233207
79033a84-f346-4a57-bebf-db4f8cdaafab
ISBN: 9781450368223
DOI: 10.1145/3336191.3371848
metadata.dc.description.sponsorship: This work has been partially supported by the DFG through the project “ACQuA: Answering Comparative Questions with Arguments” (grants BI 1544/7-1 and HA 5851/2-1) as part of the priority program “RATIO: Robust Argumentation Machines” (SPP 1999). We thank Yandex and Mail.Ru for granting access to the data. The study was partially conducted during Pavel Braslavski’s research stay at the Bauhaus-Universität Weimar in 2018 supported by the DAAD. We also thank Ekaterina Shirshakova and Valentin Dittmar for their help in question annotation.
Appears in Collections:Научные публикации, проиндексированные в SCOPUS и WoS CC

Files in This Item:
File Description SizeFormat 
2-s2.0-85079535949.pdf1,45 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.