Using WordNet to Extend FrameNet Coverage

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding


We present two methods to address the problem of sparsity in the FrameNet lexical database. The first method is based on the idea that a word that belongs to a frame is ``similar'' to the other words in that frame. We measure the similarity using a WordNet-based variant of the Lesk metric. The second method uses the sequence of synsets in WordNet hypernym trees as feature vectors that can be used to train a classifier to determine whether a word belongs to a frame or not. The extended dictionary produced by the second method was used in a system for FrameNet-based
semantic analysis and gave an improvement in recall.
We believe that the methods are useful for bootstrapping FrameNets for new languages.


Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Computer Science


  • Frame semantics, natural language processing, WordNet, FrameNet
Original languageEnglish
Title of host publicationLU-CS-TR: 2007-240
EditorsPierre Nugues, Richard Johansson
PublisherDepartment of Computer Science, Lund University
Number of pages4
ISBN (Print)978-91-976939-0-5
StatePublished - 2007
EventBuilding Frame Semantics Resources for Scandinavian and Baltic Languages - Tartu, Estonia


ConferenceBuilding Frame Semantics Resources for Scandinavian and Baltic Languages
Period2007/05/24 → …