Analysing the dictionary definitions
Computational lexicography for natural language processing
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Using the web to obtain frequencies for unseen bigrams
Computational Linguistics - Special issue on web as corpus
On learning more appropriate Selectional Restrictions
EACL '95 Proceedings of the seventh conference on European chapter of the Association for Computational Linguistics
MindNet: acquiring and structuring semantic information from text
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
Mining tables from large scale HTML texts
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
An experiment on learning appropriate Selectional Restrictions from a parsed corpus
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 2
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Finding parts in very large corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Hownet And the Computation of Meaning
Hownet And the Computation of Meaning
Semantic taxonomy induction from heterogenous evidence
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
| Hi-index | 0.00 |
It is well known that lexical knowledge sources such as WordNet, HowNet are very important to natural language processing applications. In those lexical resources, attributes play very important roles for defining and distinguishing different concepts. In this paper, we propose a novel method to automatically discover the attribute hosts of HowNet's attribute set. Given an attribute, we model the solving of its host as a problem of selectional constraint resolution. The World Wide Web is exploited as a large corpus to acquire the training data for such a model. From the training data, the attribute hosts are discovered by using a statistical measure and a semantic hierarchy. We evaluate our algorithm by comparing the result with the original hand-coded attribute specification in HowNet. Some experimental results about the performance of the method are provided.