Skip N-grams and Ranking Functions for Predicting Script Events

By Bram Jans, Steven Bethard, Ivan Vulic, Marie-Francine Moens. 2012

132 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories (2016, methodology): “Jans et al. (Jans et al., 2012) redeﬁned the test to be a text ordered sequence of events, whereas the original did not rely on text order (Chambers and Jurafsky, 2008).”
Abductive Commonsense Reasoning (2019, positive): “Abductive Reasoning Since abduction is fundamentally concerned with plausible chains of cause-and-effect, our work draws inspiration from previous works that deal with narratives such as script learning Schank & Abelson (1975) and the narrative cloze test Chambers & Jurafsky (2009); Jans et al. (201”
Constructing Narrative Event Evolutionary Graph for Script Event Prediction (2018, methodology): “• Bigram [Jans et al. , 2012] is the counting-based skip-grams model that calculates event pair relations based on bigram probabilities.”
Learning to predict script events from domain-specific text (2015, methodology): “Scoring We employ three different scoring metrics: average rank (Chambers and Jurafsky, 2008), mean reciprocal rank, and recall at 50 (Jans et al., 2012).”
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling (2020, unknown): “Scripts (Schank and Abelson, 1977) encode frequently recurring event sequences, where events are ordered by temporal relation (Chambers and Ju-rafsky, 2009), causal relation (Mostafazadeh et al., 2016b), or narrative order (Jans et al., 2012).”
Script Induction as Language Modeling (2015, methodology): “Ordered PMI ( OP ) This model is a slight variation on Unordered PMI introduced by Jans et al. (2012).”
Story Comprehension for Predicting What Happens Next (2017, methodology): “Several statistical methods have been proposed to automatically learn scripts or scripts-like structures from unstructured text (Chambers and Ju-rafsky, 2008, 2009; Jans et al., 2012; Orr et al., 2014; Pichotta and Mooney, 2014).”
Generating Reasonable and Diversified Story Ending Using Sequence to Sequence Model with Adversarial Training (2018, background): “…unsupervised induction of narrative event chains from raw newswire text, with narrative cloze as the evaluation metric, and pio-neered the recent line of work on statistical script learning (Jans et al., 2012; Pichotta and Mooney, 2014; Pichotta and Mooney, 2016b; Granroth-Wilding and Clark, 2016).”
Statistical Script Learning with Multi-Argument Events (2014, methodology): “…cooccurEvs in line 5 takes a document D and returns all ordered pairs of co-occurring events in D, where, following the 2-skip bigram model of Jans et al. (2012), and similar to Balasubramanian et al. (2012; 2013), two events a1 and a2 are said to co-occur if they occur in order, in the same…”