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We present an automated system that computes multi-cue associations and generates associated-word suggestions, using lexical co-occurrence data from a large corpus of English texts. The system performs expansion of cue words to their inflectional variants, retrieves candidate words from corpus data, finds maximal associations between candidates and cues, computes an aggregate score for each candidate, and outputs an n-best list of candidates. We present experiments using several measures of statistical association, two methods of score aggregation, ablation of resources and applying additional filters on retrieved candidates. The system achieves 18.6% precision on the COGALEX-4 shared task data. Results with additional evaluation methods are presented. We also describe an annotation experiment which suggests that the shared task may underestimate the appropriateness of candidate words produced by the corpus-based system.

ETS Lexical Associations System for the COGALEX-4 Shared Task