Benchmark for complex answer retrieval

Nanni, Federico ; Mitra, Bhaskar ; Magnusson, Matt ; Dietz, Laura

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URN: urn:nbn:de:bsz:180-madoc-427739
Document Type: Conference or workshop publication
Year of publication: 2017
Book title: ICTIR '17 : proceedings of the 3rd ACM International Conference on the Theory of Information Retrieval : October 1-4 2017, Amsterdam, Netherlands
Page range: 293-296
Conference title: ICTIR 2017
Location of the conference venue: Amsterdam, Netherlands
Date of the conference: 1-4 October 2017
Publisher: Kamps, Jaap
Place of publication: New York, NY
Publishing house: ACM
ISBN: 978-1-4503-4490-6
Related URLs:
Publication language: English
Institution: School of Business Informatics and Mathematics > Information Systems III: Enterprise Data Analysis (Ponzetto 2016-)
Subject: 004 Computer science, internet
Abstract: Providing answers to complex information needs is a challenging task. The new TREC Complex Answer Retrieval (TREC CAR) track introduces a large-scale dataset where paragraphs are to be retrieved in response to outlines of Wikipedia articles representing complex information needs. We present early results from a variety of approaches – from standard information retrieval methods (e.g., TF-IDF) to complex systems that adopt query expansion, knowledge bases and deep neural networks. The goal is to offer an overview of some promising approaches to tackle this problem.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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