Name
Abstract | ||
|---|---|---|
Information Content (IC) is an important dimension of word knowledge when assessing the similarity of two terms or word senses. The conventional way of measuring the IC of word senses is to combine knowledge of their hierarchical structure from an ontology like WordNet with statistics on their actual usage in text as derived from a large corpus. In this paper we present a wholly intrinsic measure of IC that relies on hierarchical structure alone. We report that this measure is consequently easier to calculate, yet when used as the basis of a similarity mechanism it yields judgments that correlate more closely with human assessments than other, extrinsic measures of IC that additionally employ corpus analysis. |
Year | Venue | Keywords |
|---|---|---|
2004 | FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS | semantic similarity,information content |
Field | DocType | Volume |
Semantic similarity,Ontology,Information retrieval,Computer science,Corpus analysis,Natural language processing,Artificial intelligence,WordNet,Instrumental and intrinsic value | Conference | 110 |
ISSN | Citations | PageRank |
0922-6389 | 244 | 10.24 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nuno Seco | 1 | 448 | 27.86 |
| Tony Veale | 2 | 823 | 79.63 |
| Jer Hayes | 3 | 281 | 15.32 |