dc.contributor
Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.contributor
Universitat Politècnica de Catalunya. GPLN - Grup de Processament del Llenguatge Natural
dc.contributor.author
González Pellicer, Edgar
dc.contributor.author
Turmo Borras, Jorge
dc.identifier
González, E.; Turmo, J. Unsupervised relation extraction by massive clustering. A: IEEE International Conference On Data Mining. "9th IEEE International Conference On Data Mining". Miami: 2009, p. 782-787.
dc.identifier
https://hdl.handle.net/2117/9229
dc.identifier
10.1109/ICDM.2009.81
dc.description.abstract
The goal of Information Extraction is to automatically generate structured pieces of information from the relevant information contained in text documents. Machine Learning techniques have been applied to reduce the cost of Information Extraction system adaptation. However, elements of human supervision strongly bias the learning
process. Unsupervised learning approaches can avoid these biases.
In this paper, we propose an unsupervised approach to learning for Relation Detection, based on the use of massive clustering ensembles. The results obtained on the ACE Relation Mention Detection task outperform in terms of F1 score by 5 points the state of the art of unsupervised techniques for this evaluation framework, in addition to being simpler and more flexible.
dc.description.abstract
Peer Reviewed
dc.description.abstract
Postprint (published version)
dc.format
application/pdf
dc.relation
http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5360311&queryText%3Dgonz%C3%A0lez+icdm+2009%26openedRefinements%3D*%26searchField%3DSearch+All
dc.subject
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic
dc.subject
Data mining -- Data processing
dc.subject
Information retrieval
dc.subject
Pattern clustering
dc.subject
Mineria de dades
dc.title
Unsupervised relation extraction by massive clustering
dc.type
Conference report
