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Cross-Document Event Ordering through Temporal Relation Inference and Distributional Semantic Models

  • Autores: Estela Saquete Boró Árbol académico, Borja Navarro Colorado Árbol académico
  • Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 58, 2017, págs. 61-68
  • Idioma: inglés
  • Títulos paralelos:
    • Ordenación de eventos multidocumento usando inferencia de relaciones temporales y modelos semánticos distribucionales
  • Enlaces
  • Resumen
    • español

      Este artículo se centra en estudiar la contribución que la inferencia de relaciones temporales y los modelos semánticos distribucionales hacen a la tarea de ordenación de eventos. Nuestro sistema construye automáticamente líneas de tiempo con eventos extraídos de diferentes documentos escritos en inglés. Para ello realiza primero una agrupación temporal y posteriormente una agrupación semántica. Para determinar la compatibilidad temporal se realiza una inferencia sobre las relaciones temporales entre los eventos extraídos de un sistema automático de procesamiento de información temporal. Para la compatibilidad semántica entre eventos hemos analizado dos modelos semánticos distribucionales distintos: LDA Topic Modeling y Word2Vec Word Embeddings. Ambos modelos semánticos junto con la inferencia temporal han sido evaluados bajo el marco de evaluación de SemEval 2015 Task 4 Track B. Los experimentos muestran que, usando ambos modelos se mejora el estado del arte actual, implicando un avance importante en la tarea de ordenación de eventos multidocumento.

    • English

      This paper focuses on the contribution of temporal relations inference and distributional semantic models to the event ordering task. Our system automatically builds ordered timelines of events from different written texts in English by performing first temporal clustering and then semantic clustering. In order to determine temporal compatibility, an inference from the temporal relationships between events –automatically extracted from a Temporal Information Processing system– is applied. Regarding semantic compatibility between events, we analyze two different distributional semantic models: LDA Topic modeling and Word2Vec word embeddings. Both semantic models together with the temporal inference have been evaluated within the framework of SemEval 2015 Task 4 Track B. Experiments show that, using both models, the current State of the Art is improved, showing significant advance in the Cross-Document Event Ordering task.

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