Learning to rank answers to non-factoid questions from web collections

Mihai Surdeanu, Massimiliano Ciaramita, Hugo Zaragoza

Research output: Contribution to journalArticlepeer-review

136 Scopus citations


This work investigates the use of linguistically motivated features to improve search, in particular for ranking answers to non-factoid questions. We show that it is possible to exploit existing large collections of question-answer pairs (from online social Question Answering sites) to extract such features and train ranking models which combine them effectively.We investigate a wide range of feature types, some exploiting natural language processing such as coarse word sense disambiguation, named-entity identification, syntactic parsing, and semantic role labeling. Our experiments demonstrate that linguistic features, in combination, yield considerable improvements in accuracy. Depending on the system settings we measure relative improvements of 14% to 21% in Mean Reciprocal Rank and Precision@1, providing one of the most compelling evidence to date that complex linguistic features such as word senses and semantic roles can have a significant impact on large-scale information retrieval tasks.

Original languageEnglish (US)
Pages (from-to)351-383
Number of pages33
JournalComputational Linguistics
Issue number2
StatePublished - Jun 2011

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Computer Science Applications
  • Artificial Intelligence


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