Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Bibliographic Data
| ID | 5772951 |
|---|---|
| Authors | Alexander Budanitsky (University of Toronto), Graeme Hirst (0000-0001-9482-1042, University of Toronto) |
| Year | 2006 |
| Volume | 32 |
| Issue | 1 |
| Pages | 13-47 |
| Publication date | 2006-03-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Computational Linguistics (JOURNAL) |
| Journal identifiers | ISSN: 0891-2017 • E-ISSN: 1530-9312 |
| Publisher | Association for Computational Linguistics (PUBLISHER • US) |
| DOI | 10.1162/coli.2006.32.1.13 |
| OpenAlex | W2136930489 |
| Language | EN |
| Citations received | 30 |
| References cited | 18 |
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
Information retrieval · Lexical Database · Linguistics · Machine learning · Natural language processing · Proxy (statistics · Semantic similarity · Similarity (geometry · Spelling · WordNet · Computer Science · Natural Language Processing Techniques · Text Readability and Simplification · Topic Modeling · Artificial Intelligence
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| Unique citing works | 30 |
|---|---|
| Citations per year | 1,43 |
| Citation span | 2005 - 2026 (22) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 29 |