Extracting Temporal and Causal Relations between Events

Mirza, Paramita (2016) Extracting Temporal and Causal Relations between Events. PhD thesis, University of Trento.

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Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events. In this thesis we present a framework for an integrated temporal and causal relation extraction system. We first develop a robust extraction component for each type of relations, i.e. temporal order and causality. We then combine the two extraction components into an integrated relation extraction system, CATENA---CAusal and Temporal relation Extraction from NAtural language texts---, by utilizing the presumption about event precedence in causality, that causing events must happened BEFORE resulting events. Several resources and techniques to improve our relation extraction systems are also discussed, including word embeddings and training data expansion. Finally, we report our adaptation efforts of temporal information processing for languages other than English, namely Italian and Indonesian.

Item Type:Doctoral Thesis (PhD)
Doctoral School:Information and Communication Technology
PhD Cycle:28
Subjects:Area 01 - Scienze matematiche e informatiche > INF/01 INFORMATICA
Funders:Fondazione Bruno Kessler
Repository Staff approval on:09 May 2016 10:05

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