Question Answering for the Curated Web: Tasks and Methods in QA over Knowledge Bases and Text Collections

Rishiraj Saha Roy , Avishek Anand

Question Answering for the Curated Web: Tasks and Methods in QA over Knowledge Bases and Text Collections
  • 出版商: Morgan & Claypool
  • 出版日期: 2021-10-28
  • 售價: $3,140
  • 貴賓價: 9.5 折 $2,983
  • 語言: 英文
  • 頁數: 194
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1636392407
  • ISBN-13: 9781636392400

    • 海外代購書籍(需單獨結帳)

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商品描述

Question answering (QA) systems on the Web try to provide crisp answers to information needs posed in natural language, replacing the traditional ranked list of documents. QA, posing a multitude of research challenges, has emerged as one of the most actively investigated topics in information retrieval, natural language processing, and the artificial intelligence communities today. The flip side of such diverse and active interest is that publications are highly fragmented across several venues in the above communities, making it very difficult for new entrants to the field to get a good overview of the topic.

Through this book, we make an attempt towards mitigating the above problem by providing an overview of the state-of-the-art in question answering. We cover the twin paradigms of curated Web sources used in QA tasks ? trusted text collections like Wikipedia, and objective information distilled into large-scale knowledge bases. We discuss distinct methodologies that have been applied to solve the QA problem in both these paradigms, using instantiations of recent systems for illustration. We begin with an overview of the problem setup and evaluation, cover notable sub-topics like open-domain, multi-hop, and conversational QA in depth, and conclude with key insights and emerging topics. We believe that this resource is a valuable contribution towards a unified view on QA, helping graduate students and researchers planning to work on this topic in the near future.

作者簡介

Rishiraj Saha Roy is a Senior Researcher at the Max Planck In- stitute for Informatics (MPII), Saarbruecken, Germany. He leads the research group on Question Answering (https: //qa.mpi- inf.mpg.de), that focuses on robust and interpretable solutions for answering natural language questions over structured and unstructured data. He has about six years of research experience on question answering. In recent years, he has served on the PCs of conferences like SIGIR, CIKM, WSDM, AAAI, and EMNLP, and published at venues like SIGIR, CIKM, WSDM, WWW, and NAACL. Prior to joining MPII, he worked for one and a half years as a Computer Scientist at Adobe Research. He completed his PhD as a Microsoft Research India Fellow from the Indian Institute of Technology (IIT) Kharagpur.
 

Avishek Anand is an Assistant Professor at the Leibniz Univer- sity of Hannover, Germany, and a member of the L3S Research Center, Hannover. He has also been a visiting scholar at Ama- zon Search. His research aims to develop intelligent and trans- parent machine learning approaches to help humans find rele- vant information. Specifically, he is interested in scalable and in- terpretable representation learning methods for text and graphs for problems relating to the Web and information retrieval. He holds a PhD in Computer Science from the Max Planck Institute for Informatics (MPII), Saarbruecken, Germany. He has served in the PCs of numerous Web, IR, and NLP conferences and journals, like WSDM, SIGIR, ACL, TOIS, TKDE, and TWEB. He has served on the organizing committees of conferences like ICTIR, TPDL, Dagstuhl seminars and other summer schools. His research is sup- ported by generous grants from the German Science Foundation (DFG), EU Horizon 2020, Amazon research awards, and Schufa Holding AG.

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