2008 KnowledgeDiscoveryofSemanticRel

• (Sato et al., 2008) ⇒ Issei Sato, Minoru Yoshida, and Hiroshi Nakagawa. (2008). “Knowledge Discovery of Semantic Relationships Between Words Using Nonparametric Bayesian Graph Model.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1401962

Subject Headings:

Notes

Subject Headings(s): Word Similarity Learning Task, Graph Clustering Algorithm

Notes

Paper Summary

• The paper proposes a Probabilistic Generative Graph Clustering Algorithm that discovers clusters of nodes/vertices within a Disassortative Graph and applies the algorithm to the Word Similarity Learning Task.
• The algorithm is
• The proposed algorithm differs from previous algorithms in its ability to detect soft/inclusive clusters (and to not require the number of clusters sought??)
• The proposed algorithm is applied to the task of grouping nouns with similar meaning and verbs with similar meaning.
• The proposed algorithm is empirically tested againt its ability to find words that WordNet considers to be similar.
• In general the proposed algorithm performed better than the baseline algorithms.

Questions

• Define an Assortative Graph.
• It'd be good to check-in with the graph-mining community for other relevant references.
• It'd be good to check in with the NLP community for other relevant references.
• Coudl it discover WordNet synsets?
• How could this information be used for other semantic analysis?
  • If the threshold(?) were high enough then it could discover synonyms.
  • It could discover words with more than one meaning (homonyms)

Cited By

• http://scholar.google.com/scholar?q=%22Knowledge+discovery+of+semantic+relationships+between+words+using+nonparametric+bayesian+graph+model%22+2008
• http://portal.acm.org/citation.cfm?doid=1401890.1401962&preflayout=flat#citedby

Quotes

Abstract

We developed a model based on nonparametric Bayesian modeling for automatic discovery of semantic relationships between words taken from a corpus. It is aimed at discovering semantic knowledge about words in particular domains, which has become increasingly important with the growing use of text mining, information retrieval, and speech recognition. The subject-predicate structure is taken as a syntactic structure with the noun as the subject and the verb as the predicate. This structure is regarded as a graph structure. The generation of this graph can be modeled using the hierarchical Dirichlet process and the Pitman-Yor process. The probabilistic generative model we developed for this graph structure consists of subject-predicate structures extracted from a corpus. Evaluation of this model by measuring the performance of graph clustering based on WordNet similarities demonstrated that it outperforms other baseline models.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 KnowledgeDiscoveryofSemanticRel	Hiroshi Nakagawa Issei Sato Minoru Yoshida			Knowledge Discovery of Semantic Relationships Between Words Using Nonparametric Bayesian Graph Model		KDD-2008 Proceedings	http://www.r.dl.itc.u-tokyo.ac.jp/~nakagawa/academic-res/KDD2008.pdf	10.1145/1401890.1401962		2008