Corpus ID: 237503586

Learning Constraints and Descriptive Segmentation for Subevent Detection

  title={Learning Constraints and Descriptive Segmentation for Subevent Detection},
  author={Haoyu Wang and Hongming Zhang and Muhao Chen and Dan Roth},
Event mentions in text correspond to realworld events of varying degrees of granularity. The task of subevent detection aims to resolve this granularity issue, recognizing the membership of multi-granular events in event complexes. Since knowing the span of descriptive contexts of event complexes helps infer the membership of events, we propose the task of event-based text segmentation (EVENTSEG) as an auxiliary task to improve the learning for subevent detection. To bridge the two tasks… Expand

Figures and Tables from this paper


Joint Constrained Learning for Event-Event Relation Extraction
This work proposes a joint constrained learning framework for modeling event-event relations that enforces logical constraints within and across multiple temporal and subevent relations by converting these constraints into differentiable learning objectives. Expand
Weakly Supervised Subevent Knowledge Acquisition
A weakly supervised approach to extract subevent relation tuples from text and build the first large scale subevent knowledge base, which has been shown useful for discourse analysis and identifying a range of event-event relations. Expand
HiEve: A Corpus for Extracting Event Hierarchies from News Stories
This work presents HiEve, a corpus for recognizing relations of spatiotemporal containment between events, in which the narratives are represented as hierarchies of events based on relations of spatial and temporal containment. Expand
Learning towards Abstractive Timeline Summarization
This paper proposes a memory-based timeline summarization model (MTS), which tends to concisely paraphrase the information in the time-stamped events and achieves the state-of-the-art performance in terms of both automatic and human evaluations. Expand
Analogous Process Structure Induction for Sub-event Sequence Prediction
An Analogous Process Structure Induction APSI framework is proposed, which leverages analogies among processes and conceptualization of sub- event instances to predict the whole sub-event sequence of previously unseen open-domain processes. Expand
Detecting Subevents using Discourse and Narrative Features
This work presents a supervised model for automatically identifying when one event is a subevent of another, and introduces several novel features, in particular discourse and narrative features, that significantly improve upon prior state-of-the-art performance. Expand
Detecting Subevent Structure for Event Coreference Resolution
A novel two-stage approach to finding and improving subevent structures by introducing a multiclass logistic regression model that can detect subevent relations in addition to full coreference and proposing a method to improve subevent structure based on subevent clusters detected by the model. Expand
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling
This work proposes a new Event Graph Schema, where two event types are connected through multiple paths involving entities that fill important roles in a coherent story, and introduces Path Language Model, an auto-regressive language model trained on event-event paths, to select salient and coherent paths to probabilistically construct these graph schemas. Expand
BeamSeg: A Joint Model for Multi-Document Segmentation and Topic Identification
The model implements lexical cohesion in an unsupervised Bayesian setting by drawing from the same language model segments with the same topic by using a dynamic Dirichlet prior that takes into account data contributions from other topics. Expand
Richer Event Description: Integrating event coreference with temporal, causal and bridging annotation
The annotation methodology for the Richer Event Descriptions corpus is described, which annotates entities, events, times, their coreference and partial coreference relations, and the temporal, causal and subevent relationships between the events. Expand