CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
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Semantic features are theoretical units of meaning-holding components which are used for representing word meaning. These features play a vital role in determining the kind of lexical relation which exists between words in a language. Although such model of meaning representation has numerous applications in various fields, the manual derivation of semantic features is a cumbersome and time consuming task. We aim to elevate this process by developing an automated semantic feature extraction system based on ontological models. Such an approach will provide explicit word meaning representation, and enable the computation of lexical relations such as synonym and antonymy. This paper describes the design and implementation of a prototype system used for automatically deriving componential formulae, and computing lexical relations between words from a given OWL ontology. The system has been tested on a number of ontologies, both English and Arabic. Results of the evaluation indicate that the system was able to provide necessary componential formulae for highly-axiomed ontologies. With regards to computing lexical relations, the system performs better when predicting antonyms, with an average precision of 40%, and an average recall of 75%. We have also found a strong relation between ontology expressivity and system performance.