Benchmark for Complex Answer Retrieval

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.

[1]  James P. Callan,et al.  EsdRank: Connecting Query and Documents through External Semi-Structured Data , 2015, CIKM.

[2]  Nick Craswell,et al.  Learning to Match using Local and Distributed Representations of Text for Web Search , 2016, WWW.

[3]  W. Bruce Croft,et al.  Quality-biased ranking of web documents , 2011, WSDM '11.

[4]  James Allan,et al.  Entity query feature expansion using knowledge base links , 2014, SIGIR.

[5]  Hang Li.  Learning to Rank for Information Retrieval and Natural Language Processing , 2011, Synthesis Lectures on Human Language Technologies.

[6]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[7]  Xitong Liu,et al.  Latent entity space: a novel retrieval approach for entity-bearing queries , 2015, Information Retrieval Journal.

[8]  Paolo Ferragina,et al.  TAGME: on-the-fly annotation of short text fragments (by wikipedia entities) , 2010, CIKM.

[9]  Bhaskar Mitra,et al.  Neural Models for Information Retrieval , 2017, ArXiv.

[10]  Laura Dietz,et al.  An Interface Sketch for Queripidia: Query-driven Knowledge Portfolios from the Web , 2015, ESAIR@CIKM.

[11]  Tie-Yan Liu,et al.  Learning to rank for information retrieval , 2009, SIGIR.

[12]  James Allan,et al.  Frontiers, challenges, and opportunities for information retrieval: Report from SWIRL 2012 the second strategic workshop on information retrieval in Lorne , 2012, SIGF.

[13]  Krisztian Balog,et al.  Exploiting Entity Linking in Queries for Entity Retrieval , 2016, ICTIR.

[14]  ChengXiang Zhai,et al.  Tapping into knowledge base for concept feedback: leveraging conceptnet to improve search results for difficult queries , 2012, WSDM '12.

[15]  Regina Barzilay,et al.  Automatically Generating Wikipedia Articles: A Structure-Aware Approach , 2009, ACL.

[16]  Maarten de Rijke,et al.  Mining, Ranking and Recommending Entity Aspects , 2015, SIGIR.

[17]  Prasenjit Mitra,et al.  WikiKreator: Improving Wikipedia Stubs Automatically , 2015, ACL.

[18]  Oren Kurland,et al.  Utilizing Passage-Based Language Models for Document Retrieval , 2008, ECIR.

[19]  James Allan,et al.  Passage Reranking for Question Answering Using Syntactic Structures and Answer Types , 2011, ECIR.

[20]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[21]  Heiko Paulheim,et al.  RDF2Vec: RDF Graph Embeddings for Data Mining , 2016, SEMWEB.

[22]  Oren Kurland,et al.  Document Retrieval Using Entity-Based Language Models , 2016, SIGIR.

[23]  Roi Blanco,et al.  Finding support sentences for entities , 2010, SIGIR.

[24]  W. Bruce Croft,et al.  Effective query formulation with multiple information sources , 2012, WSDM '12.

[25]  Md. Mustafizur Rahman,et al.  Neural Information Retrieval: A Literature Review , 2016, ArXiv.

[26]  W. Bruce Croft,et al.  Relevance-Based Language Models , 2001, SIGIR '01.