Unsupervised relation detection using automatic alignment of query patterns extracted from knowledge graphs and query click logs

Panupong Pasupat, Dilek Hakkani-Tür

Research output: Contribution to journalConference articlepeer-review

Abstract

Traditional methods for building spoken language understanding systems require manual rules or annotated data, which are expensive. In this work, we present an unsupervised method for bootstrapping a relation classifier, which identifies the knowledge graph relations present in an input query. Unlike existing work, we utilize only one knowledge graph entity instead of two for mining relevant query patterns from query click logs. As a result, the mined patterns can be used to infer both explicit relations (where the objects of the relations are expressed in the queries) and implicit relations (where the objects of the relations are being asked about). Using only the mined queries, the final classifier achieves an F-measure of 55.5%, which is significantly higher than the previous unsupervised learning baselines.

Original languageEnglish (US)
Pages (from-to)2714-2718
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2015-January
StatePublished - 2015
Externally publishedYes
Event16th Annual Conference of the International Speech Communication Association, INTERSPEECH 2015 - Dresden, Germany
Duration: Sep 6 2015Sep 10 2015

Keywords

  • Conversational understanding systems
  • Relation detection
  • Search query click logs
  • Semantic graph
  • Unsupervised learning

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modeling and Simulation

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