A graph partitioning approach to coreference resolution
This report presents a graph partitioning approach given a set of constraints to resolve coreferences. Coreference resolution is the task of determining which referring expressions in a discourse refer to the same entity. Coreference resolution is a natural language processing task which has a direc...
| 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:2117/86945 |
| Acceso en línea: | https://hdl.handle.net/2117/86945 |
| Access Level: | acceso abierto |
| Palabra clave: | Coreference Anaphora Natural language processing Text mining Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | This report presents a graph partitioning approach given a set of constraints to resolve coreferences. Coreference resolution is the task of determining which referring expressions in a discourse refer to the same entity. Coreference resolution is a natural language processing task which has a direct effect on the field of Text Mining and its related areas such as Information Extraction, Question Answering, Summarization, Machine Translation. This report summarizes the research done in coreference resolution and presents our machine learning graph-based system and our baseline with preliminary results in comparison with other machine learning systems in the state of the art. |
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