Generalized framework for syntax-based relation mining

Bonaventura Coppola, Alessandro Moschitti, Daniele Pighin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Supervised approaches to Data Mining are particularly appealing as they allow for the extraction of complex relations from data objects. In order to facilitate their application in different areas, ranging from protein to protein interaction in bioinformatics to text mining in computational linguistics research, a modular and general mining framework is needed. The major constraint to the generalization process concerns the feature design for the description of relational data. In this paper, we present a machine learning framework for the automatic mining of relations, where the target objects are structurally organized in a tree. Object types are generalized by means of the use of roles, whereas the relation properties are described by means of the underlying tree structure. The latter is encoded in the learning algorithm thanks to kernel methods for structured data, which represent structures in terms of their all possible subparts. This approach can be applied to any kind of data disregarding their very nature. Experiments with Support Vector Machines on two text mining datasets for relation extraction, i.e. the PropBank and FrameNet corpora, show both that our approach is general, and that it reaches state-of-the-art accuracy.

Original languageEnglish
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages153-162
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2008
Externally publishedYes
Event8th IEEE International Conference on Data Mining, ICDM 2008 - Pisa, Italy
Duration: 15 Dec 200819 Dec 2008

Other

Other8th IEEE International Conference on Data Mining, ICDM 2008
CountryItaly
CityPisa
Period15/12/0819/12/08

Fingerprint

Computational linguistics
Proteins
Bioinformatics
Learning algorithms
Support vector machines
Data mining
Learning systems
Experiments

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Coppola, B., Moschitti, A., & Pighin, D. (2008). Generalized framework for syntax-based relation mining. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 153-162). [4781110] https://doi.org/10.1109/ICDM.2008.153

Generalized framework for syntax-based relation mining. / Coppola, Bonaventura; Moschitti, Alessandro; Pighin, Daniele.

Proceedings - IEEE International Conference on Data Mining, ICDM. 2008. p. 153-162 4781110.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Coppola, B, Moschitti, A & Pighin, D 2008, Generalized framework for syntax-based relation mining. in Proceedings - IEEE International Conference on Data Mining, ICDM., 4781110, pp. 153-162, 8th IEEE International Conference on Data Mining, ICDM 2008, Pisa, Italy, 15/12/08. https://doi.org/10.1109/ICDM.2008.153
Coppola B, Moschitti A, Pighin D. Generalized framework for syntax-based relation mining. In Proceedings - IEEE International Conference on Data Mining, ICDM. 2008. p. 153-162. 4781110 https://doi.org/10.1109/ICDM.2008.153
Coppola, Bonaventura ; Moschitti, Alessandro ; Pighin, Daniele. / Generalized framework for syntax-based relation mining. Proceedings - IEEE International Conference on Data Mining, ICDM. 2008. pp. 153-162
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