Generative Event Schema Induction with Entity Disambiguation

By Kiem-Hieu Nguyen, Xavier Tannier, Olivier Ferret, Romaric Besançon. 2015

69 citations recorded by the source corpus.

This paper presents a generative model to event schema induction. Previous methods in the literature only use head words to represent entities. However, elements other than head words contain useful information. For instance, an armed man is more discriminative than man. Our model takes into account this information and precisely represents it using probabilistic topic distributions. We illustrate that such information plays an important role in parameter estimation. Mostly, it makes topic distributions more coherent and more discriminative. Experimental results on benchmark dataset empirically confirm this enhancement.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories (2016, methodology): “Formal probabilistic models have also been proposed to learn event schemas and frames (Che-ung et al., 2013; Bamman et al., 2013; Chambers, 2013; Nguyen et al., 2015).”
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling (2020, unknown): “Atomic event schema induction methods (Chambers, 2013; Che-ung et al., 2013; Nguyen et al., 2015; Huang et al., 2016; Sha et al., 2016; Yuan et al., 2018) focus on discovering event types and argument roles of individual atomic events.”
CaTeRS: Causal and Temporal Relation Scheme for Semantic Annotation of Event Structures (2016, background): “Given that developing hand-built scripts is extremely time-consuming, there is a serious need for automatically induced scripts (Chambers and Jurafsky, 2008; Chambers and Jurafsky, 2009; Balasubramanian et al., 2013; Cheung et al., 2013; Nguyen et al., 2015).”
Semi-supervised New Event Type Induction and Event Detection (2020, background): “Some researchers explore probabilistic generative methods (Chambers, 2013; Nguyen et al., 2015; Yuan et al., 2018; Liu et al., 2019) or ad-hoc clustering-based algorithms (Huang et al.”
Story Comprehension for Predicting What Happens Next (2017, background): “There has also been work on representing events in a structured manner using schemas, which are learned probabilistically (Chambers, 2013; Cheung et al., 2013; Nguyen et al., 2015), using graphs (Balasubramanian et al.”