Título: | DISTANT SUPERVISION FOR RELATION EXTRACTION USING ONTOLOGY CLASS HIERARCHY-BASED FEATURES | ||||||||||||||||||||||||||||||||||||
Autor: |
PEDRO HENRIQUE RIBEIRO DE ASSIS |
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Colaborador(es): |
MARCO ANTONIO CASANOVA - Orientador |
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Catalogação: | 18/MAR/2015 | Língua(s): | ENGLISH - UNITED STATES |
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Tipo: | TEXT | Subtipo: | THESIS | ||||||||||||||||||||||||||||||||||
Notas: |
[pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio. [en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio. |
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Referência(s): |
[pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=24296&idi=1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=24296&idi=2 |
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DOI: | https://doi.org/10.17771/PUCRio.acad.24296 | ||||||||||||||||||||||||||||||||||||
Resumo: | |||||||||||||||||||||||||||||||||||||
Relation extraction is a key step for the problem of rendering a structure
from natural language text format. In general, structures are composed by entities
and relationships among them. The most successful approaches on relation
extraction apply supervised machine learning on hand-labeled corpus for creating
highly accurate classifiers. Although good robustness is achieved, hand-labeled
corpus are not scalable due to the expensive cost of its creation. In this work we
apply an alternative paradigm for creating a considerable number of examples of
instances for classification. Such method is called distant supervision. Along with
this alternative approach we adopt Semantic Web ontologies to propose and use
new features for training classifiers. Those features are based on the structure and
semantics described by ontologies where Semantic Web resources are defined.
The use of such features has a great impact on the precision and recall of our final
classifiers. In this work, we apply our theory on corpus extracted from Wikipedia.
We achieve a high precision and recall for a considerable number of relations.
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