2006/03/01 by Alexander Budanitsky, Graeme Hirst · 5 citations
Computer Science · #Natural Language Processing Techniques #Topic Modeling #Text Readability and Simplification
paper · pdf · doi:10.1162/coli.2006.32.1.13
openalex publication_date 2006/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content-based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik. In addition, we explain why distributional similarity is not an adequate proxy for lexical semantic relatedness.