Semantic Relations between Events and their Time, Locations and Participants for Event Coreference Resolution

In this study, we measure the contribution of different event components and particular semantic relations to the task of event coreference resolution. First we calculate what event times, locations and participants add to event coreference resolution. Secondly, we analyze the contribution by hyponymy and granularity within the participant component. Coreference of events is then calculated from the coreference match scores of each event component. Coreferent action candidates are accordingly filtered based on compatibility of their time, locations, or participants. We report the success rates of our experiments on a corpus annotated with coreferent events.

[1]  Sanda M. Harabagiu,et al.  A Linguistic Resource for Discovering Event Structures and Resolving Event Coreference , 2008, LREC.

[2]  Sanda M. Harabagiu,et al.  Unsupervised Event Coreference Resolution with Rich Linguistic Features , 2010, ACL.

[3]  M. Felisa Verdejo,et al.  Events are Not Simple: Identity, Non-Identity, and Quasi-Identity , 2013, EVENTS@NAACL-HLT.

[4]  Simone Paolo Ponzetto,et al.  Exploiting Semantic Role Labeling, WordNet and Wikipedia for Coreference Resolution , 2006, NAACL.

[5]  Blake Howald,et al.  The Use of Granularity in Rhetorical Relation Prediction , 2012, *SEMEVAL.

[6]  Breck Baldwin,et al.  Algorithms for Scoring Coreference Chains , 1998 .

[7]  Eduard Hovy,et al.  Identity, non-identity, and near-identity: Addressing the complexity of coreference , 2011 .

[8]  Claire Gardent,et al.  Improving Machine Learning Approaches to Coreference Resolution , 2002, ACL.

[9]  Jerry R. Hobbs,et al.  Applications and Discovery of Granularity Structures in Natural Language Discourse , 2011, AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning.

[10]  Jian Su,et al.  A Unified Event Coreference Resolution by Integrating Multiple Resolvers , 2011, IJCNLP.

[11]  Xiaoqiang Luo,et al.  On Coreference Resolution Performance Metrics , 2005, HLT.

[12]  Piek T. J. M. Vossen,et al.  Event Models for Historical Perspectives: Determining Relations between High and Low Level Events in Text, Based on the Classification of Time, Location and Participants. , 2010, LREC.

[13]  Heeyoung Lee,et al.  Joint Entity and Event Coreference Resolution across Documents , 2012, EMNLP.

[14]  Christiane Fellbaum,et al.  Combining Local Context and Wordnet Similarity for Word Sense Identification , 1998 .

[15]  Sandra Kübler,et al.  Singletons and Coreference Resolution Evaluation , 2011, RANLP.

[16]  Véronique Malaisé,et al.  Design and use of the Simple Event Model (SEM) , 2011, J. Web Semant..

[17]  M. R E C A S E,et al.  BLANC: Implementing the Rand index for coreference evaluation , 2010, Natural Language Engineering.

[18]  Nianwen Xue,et al.  CoNLL-2011 Shared Task: Modeling Unrestricted Coreference in OntoNotes , 2011, CoNLL Shared Task.

[19]  Jerry R. Hobbs,et al.  Granularity in Natural Language Discourse , 2011, IWCS.

[20]  Lynette Hirschman,et al.  A Model-Theoretic Coreference Scoring Scheme , 1995, MUC.

[21]  Sanda M. Harabagiu,et al.  RESOLUTION , 1977, Monatsschrift für Kriminologie und Strafrechtsreform.

[22]  Piek T. J. M. Vossen,et al.  Historical Event Extraction from Text , 2011, LaTeCH@ACL.

[23]  Piek Vossen,et al.  Using Semantic Relations to Solve Event Coreference in Text , 2012 .

[24]  Vincent Ng,et al.  Machine Learning for Coreference Resolution: From Local Classification to Global Ranking , 2005, ACL.