Joint Inference for Event Timeline Construction

By Quang Xuan Do, Wei Dang Lu, Dan Roth. 2012

150 citations recorded by the source corpus.

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Citations in the corpus

Reasoning about Quantities in Natural Language (2015, methodology): “There has also been some work on quantities in specific domains, such as the temporal domain, the most significant being the TimeML project (Pustejovsky et al., 2003; Saur et al., 2005; PrattHartmann, 2005; Do et al., 2012).”
Modeling Biological Processes for Reading Comprehension (2014, background): “Event-event relations have been mostly studied from the perspective of temporal ordering; e.g., (Chambers and Jurafsky, 2008; Yoshikawa et al., 2009; Do et al., 2012; Mc-Closky and Manning, 2012).”
Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding (2019, unknown): “Early attempts by Mani et al. (2006); Verhagen et al.”
Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction (2019, unknown): “(2011); Do et al. (2012); Ning et al.”
Temporal Reasoning on Implicit Events from Distant Supervision (2020, methodology): “Within-Sentence Extraction We collect start time comparisons between pairs of events heuristically from free-text using “before/after" keywords (following much prior work in temporal modeling and extraction (Do et al., 2012)).”
Joint Reasoning for Temporal and Causal Relations (2018, methodology): “To get s ee ( · ) and s et ( · ) , we trained classiﬁers us-ing the averaged perceptron algorithm (Freund and Schapire, 1998) and the same set of features used in (Do et al., 2012; Ning et al., 2017), and then used the soft-max scores in those scoring functions.”
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions (2020, background): “Research on temporal relations often focuses on algorithmic improvement, such as structured inference (Do et al., 2012; Cham-bers et al., 2014; Ning et al., 2018a), structured learning (Leeuwenberg and Moens, 2017; Ning et al., 2017), and neural networks (Dligach et al., 2017; Lin et al., 2017;…”
Event Detection and Co-reference with Minimal Supervision (2016, background): “One of the key research directions is to extend this unsupervised approach to a range of other relations among events, including temporal and causality relations, as is (Do et al., 2011; Do et al., 2012).”
Dense Event Ordering with a Multi-Pass Architecture (2014, background): “In Do et al. (2012), “the anno-tator was not required to annotate all pairs of event mentions, but as many as possible”, and then more relations were automatically inferred after the annotation was complete.”
A Multi-Axis Annotation Scheme for Event Temporal Relations (2018, methodology): “In the future, more data can be collected and used with advanced techniques such as ILP (Do et al., 2012), structured learning (Ning et al.”
Deep Structured Neural Network for Event Temporal Relation Extraction (2019, methodology): “generally easier to predict using rule-based classifiers or date normalization technique (Do et al., 2012; Chambers et al., 2014; Mirza and Tonelli, 2016).”
Joint Constrained Learning for Event-Event Relation Extraction (2020, background): “This is not only at the core of text understanding, but is also beneﬁcial to various applications such as question answering (Khashabi et al., 2018), narrative prediction (Chaturvedi et al., 2017), timeline construction (Do et al., 2012a) and summarization (Daum´e III and Marcu, 2006).”