Word sense disambiguation for free-text indexing using a massive semantic network
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Contextual correlates of synonymy
Communications of the ACM
Placing search in context: the concept revisited
Proceedings of the 10th international conference on World Wide Web
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Extracting semantic hierarchies from a large on-line dictionary
ACL '85 Proceedings of the 23rd annual meeting on Association for Computational Linguistics
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
| Hi-index | 0.00 |
We re-investigate the rationale for and the effectiveness of adopting the notions of depth and density in WordNet-based semantic similarity measures. We show that the intuition for including these notions in WordNet-based similarity measures does not always stand up to empirical examination. In particular, the traditional definitions of depth and density as ordinal integer values in the hierarchical structure of WordNet does not always correlate with human judgment of lexical semantic similarity, which imposes strong limitations on their contribution to an accurate similarity measure. We thus propose several novel definitions of depth and density, which yield significant improvement in degree of correlation with similarity. When used in WordNet-based semantic similarity measures, the new definitions consistently improve performance on a task of correlating with human judgment.