Similarity-Based Models of Word Cooccurrence Probabilities
Machine Learning - Special issue on natural language learning
A technique for computer detection and correction of spelling errors
Communications of the ACM
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Noun classification from predicate-argument structures
ACL '90 Proceedings of the 28th annual meeting on Association for Computational Linguistics
Automatic semantic classification for Chinese unknown compound nouns
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
A graph model for unsupervised lexical acquisition
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Co-occurrence Retrieval: A Flexible Framework for Lexical Distributional Similarity
Computational Linguistics
Feature vector quality and distributional similarity
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity
ICSC '07 Proceedings of the International Conference on Semantic Computing
Graph-based word clustering using a web search engine
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Improving word sense disambiguation in lexical chaining
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Synonym extraction using a semantic distance on a dictionary
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
Graph-based clustering for computational linguistics: a survey
TextGraphs-5 Proceedings of the 2010 Workshop on Graph-based Methods for Natural Language Processing
| Hi-index | 0.00 |
This paper presents a method for semantic classification of onomatopoetic words like "[Abstract contained text which could not be displayed.] (hum)" and "[Abstract contained text which could not be displayed.] (clip clop)" which exist in every language, especially Japanese being rich in onomatopoetic words. We used a graph-based clustering algorithm called Newman clustering. The algorithm calculates a simple quality function to test whether a particular division is meaningful. The quality function is calculated based on the weights of edges between nodes. We combined two different similarity measures, distributional similarity, and orthographic similarity to calculate weights. The results obtained by using the Web data showed a 9.0% improvement over the baseline single distributional similarity measure.