Word sense ranking based on semantic similarity and graph entropy
In this paper we propose a system for the recommendation of tagged pictures obtained from the Web. The system, driven by user feedback, executes an abductive reasoning (based on WordNet synset semantic relations) that is able to iteratively lead to new concepts which progressively represent the cogn...
| Autores: | , , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2009 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/14274 |
| Acceso en línea: | https://hdl.handle.net/2117/14274 |
| Access Level: | acceso abierto |
| Palabra clave: | WordNet synset semantic relations Abductive reasoning Raonament abductiu Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | In this paper we propose a system for the recommendation of tagged pictures obtained from the Web. The system, driven by user feedback, executes an abductive reasoning (based on WordNet synset semantic relations) that is able to iteratively lead to new concepts which progressively represent the cognitive creative user state. Furthermore we design a selection mechanism to pick the most relevant abductive inferences by mixing a topological graph analysis together with a semantic similitude measure. |
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