Controlling information aggregation for complex question answering

Heeyoung Kwon, Harsh Trivedi, Peter Jansen, Mihai Surdeanu, Niranjan Balasubramanian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


Complex question answering, the task of answering complex natural language questions that rely on inference, requires the aggregation of information from multiple sources. Automatic aggregation often fails because it combines semantically unrelated facts leading to bad inferences. This paper proposes methods to address this inference drift problem. In particular, the paper develops unsupervised and supervised mechanisms to control random walks on Open Information Extraction (OIE) knowledge graphs. Empirical evaluation on an elementary science exam benchmark shows that the proposed methods enables effective aggregation even over larger graphs and demonstrates the complementary value of information aggregation for answering complex questions.

Original languageEnglish (US)
Title of host publicationAdvances in Information Retrieval - 40th European Conference on IR Research, ECIR 2018, Proceedings
EditorsLeif Azzopardi, Gabriella Pasi, Allan Hanbury, Benjamin Piwowarski
Number of pages8
ISBN (Print)9783319769400
StatePublished - 2018
Event40th European Conference on Information Retrieval, ECIR 2018 - Grenoble, France
Duration: Mar 26 2018Mar 29 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10772 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other40th European Conference on Information Retrieval, ECIR 2018

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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