A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution

@article{Yang2015AHD,
  title={A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution},
  author={Bishan Yang and Claire Cardie and P. Frazier},
  journal={Transactions of the Association for Computational Linguistics},
  year={2015},
  volume={3},
  pages={517-528}
}
We present a novel hierarchical distance-dependent Bayesian model for event coreference resolution. While existing generative models for event coreference resolution are completely unsupervised, our model allows for the incorporation of pairwise distances between event mentions — information that is widely used in supervised coreference models to guide the generative clustering processing for better event clustering both within and across documents. We model the distances between event mentions… 
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization
TLDR
This work proposes a neural network architecture with novel Clustering-Oriented Regularization (CORE) terms in the objective function that encourages the model to create embeddings of event mentions that are amenable to clustering.
Event Coreference Resolution Using Neural Network Classifiers
This paper presents a neural network classifier approach to detecting both within- and cross- document event coreference effectively using only event mention based features. Our approach does not
Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures
TLDR
This paper explicitly model correlations between the main event chains of a document with topic transition sentences, inter-coreference chain correlations, event mention distributional characteristics and sub-event structure, and use them with scores obtained from a local coreference relation classifier for jointly resolving multiple event chains in a document.
Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions
TLDR
An alternating optimization method for inference that when clustering event mentions, considers the uncertainty of the clustering of entity mentions and vice-versa, and it is shown that the proposed joint model provides empirical advantages over state-of-the-art independent and joint models.
Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events
We introduce a novel iterative approach for event coreference resolution that gradually builds event clusters by exploiting inter-dependencies among event mentions within the same chain as well as
Precision Event Coreference Resolution Using Neural Network Classifiers
TLDR
This paper presents a neural network classifier approach to detecting precise within-document (WD) and cross- document (CD) event coreference clusters effectively using only event mention based features and uses no sophisticated clustering approach.
Learning Antecedent Structures for Event Coreference Resolution
  • Jing Lu, Vincent Ng
  • Computer Science
    2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)
  • 2017
TLDR
This work views event coreference as a structured prediction task, where it proposes a probabilistic model that selects an antecedent for each event mention in a given document in a collective manner.
Joint Learning for Event Coreference Resolution
TLDR
This is the first attempt to train a mention-ranking model and employ event anaphoricity for event coreference, and achieves the best results to date on the KBP 2016 English and Chinese datasets.
Joint Inference for Event Coreference Resolution
TLDR
This work proposes a novel solution where rich features are implicitly encoded into the model by augmenting the MLN distribution with low dimensional unit clauses, and achieves state-of-the-art results on two standard evaluation corpora.
Event Coreference Resolution with Non-Local Information
We present two extensions to a state-of-theart joint model for event coreference resolution, which involve incorporating (1) a supervised topic model for improving trigger detection by providing
...
...

References

SHOWING 1-10 OF 44 REFERENCES
Unsupervised Coreference Resolution in a Nonparametric Bayesian Model
We present an unsupervised, nonparametric Bayesian approach to coreference resolution which models both global entity identity across a corpus as well as the sequential anaphoric structure within
Unsupervised Event Coreference Resolution with Rich Linguistic Features
TLDR
A new class of nonparametric Bayesian models designed with the purpose of clustering complex linguistic objects can be effectively applied to an open-domain event coreference task.
A Pairwise Event Coreference Model, Feature Impact and Evaluation for Event Coreference Resolution
TLDR
The problem of event coreference resolution in the ACE program is formally state, an agglomerative clustering algorithm for the task is presented, the feature impact in theevent coreference model is explored, and three evaluation metrics that were previously adopted in entity coreferenceresolution are compared.
Unsupervised Event Coreference Resolution
TLDR
A new class of unsupervised, nonparametric Bayesian models with the purpose of probabilistically inferring coreference clusters of event mentions from a collection of unlabeled documents is described, which combines an infinite latent class model with a discrete time series model.
Joint Entity and Event Coreference Resolution across Documents
TLDR
A novel coreference resolution system that models entities and events jointly that handles nominal and verbal events as well as entities, and the joint formulation allows information from event coreference to help entity coreference, and vice versa.
Supervised Within-Document Event Coreference using Information Propagation
TLDR
A supervised method for event coreference resolution is presented that uses a rich feature set and propagates information alternatively between events and their arguments, adapting appropriately for each type of argument.
Event coreference for information extraction
We propose a general approach for performing event coreference and for constructing complex event representations, such as those required for information extraction tasks. Our approach is based on a
A Discriminative Hierarchical Model for Fast Coreference at Large Scale
TLDR
A novel discriminative hierarchical model that recursively partitions entities into trees of latent sub-entities providing a highly compact, information-rich structure for reasoning about entities and coreference uncertainty at massive scales is proposed.
Coreference Resolution in a Modular, Entity-Centered Model
TLDR
This generative, model-based approach in which each of these factors is modularly encapsulated and learned in a primarily unsu-pervised manner is presented, resulting in the best results to date on the complete end-to-end coreference task.
Noun Phrase Coreference as Clustering
TLDR
A new, unsupervised algorithm for noun phrase coreference resolution that appears to provide a flexible mechanism for coordinating the application of context-independent and context-dependent coreference constraints and preferences for accurate partitioning of noun phrases into coreference equivalence classes.
...
...