A Clustering Approach for the Nearly Unsupervised Recognition of Nonliteral Language, EACL-2006

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Citation

Birke, J. and A. Sarkar. 2006. A clustering approach for the nearly unsupervised recognition of nonliteral language. In Proceedings of EACL-06, pages 329–336.

Online Version

pdf link to the paper

Method Summary

  • TroFi (TropeFinder) System
  1. Task: Classifying literal and nonliteral usages of verbs
  2. Approach: Use nearly unsupervised word-sense disambiguation and * clustering techniques
  • Processing Steps
  1. KE Algorithm: Similarity-based word-sense disambiguation algorithm
    • Similarities are calculated between:
      1. Sentences containing the word we wish to disambiguate (the target word)
      2. Collections of seed sentences (feedback sets)
  2. Clean the Feedback Sets
    • In order to remove false attraction
    • 4 Principle of Scrubbing
  1. Learning & Voting


  • Nearly Unsupervised Word-Sense Disambiguation


  • Clustering


    1. Use sentential context instead of selectional constraint violations or paths in semantic hierarchies

It also uses literal and nonliteral seed sets acquired and cleaned without human supervision in order to bootstrap learning.

We adapt a word-sense disambiguation algorithm to our task and augment it with multiple seed set learners, a voting schema, and additional features like SuperTags and extrasentential context.

Detailed experiments on hand-annotated data show that our enhanced algorithm outperforms the baseline by 24.4%.

Using the TroFi algorithm, we also build the TroFi Example Base, an extensible resource of annotated literal/nonliteral examples which is freely available to the NLP research community.



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Discussion

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