A deep-learning approach to mining conditions
A condition is a constraint that determines when a consequent holds. Mining them in text is paramount to understand many sentences properly. In the literature, there are a few pattern-based proposals that fall short regarding recall because it is not easy to characterise unusual ways to express cond...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universidad de Sevilla (US) |
| Repositorio: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/143218 |
| Acceso en línea: | https://hdl.handle.net/11441/143218 https://doi.org/10.1016/j.knosys.2019.105422 |
| Access Level: | acceso abierto |
| Palabra clave: | Natural language processing Text mining Condition mining Neural networks |
| Sumario: | A condition is a constraint that determines when a consequent holds. Mining them in text is paramount to understand many sentences properly. In the literature, there are a few pattern-based proposals that fall short regarding recall because it is not easy to characterise unusual ways to express conditions with hand-crafted patterns; there is one machine-learning proposal that is bound to the Japanese language, requires specific-purpose dictionaries, taxonomies, and heuristics, works on opinion sentences only, and was evaluated very shallowly. In this article, we present a deep-learning proposal to mine conditions that does not have any of the previous drawbacks; furthermore, we have performed a comprehensive experimental study on a large multi-lingual dataset on many common topics; our conclusion is that our proposals are similar to the state of the art in terms of precision, but improve recall enough to beat them in terms of F1 score. |
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