Global Inference for Bridging Anaphora Resolution

We present the first work on antecedent selection for bridging resolution without restrictions on anaphor or relation types. Our model integrates global constraints on top of a rich local feature set in the framework of Markov logic networks. The global model improves over the local one and both strongly outperform a reimplementation of prior work.

[1]  Ido Dagan,et al.  Assessing the Role of Discourse References in Entailment Inference , 2010, ACL.

[2]  Renata Vieira,et al.  An Empirically-based System for Processing Definite Descriptions , 2000, CL.

[3]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[4]  Emmanuel Lassalle,et al.  Leveraging Different Meronym Discovery Methods for Bridging Resolution in French , 2011, DAARC.

[5]  Carina Silberer,et al.  Casting Implicit Role Linking as an Anaphora Resolution Task , 2012, *SEMEVAL.

[6]  Kari Fraurud,et al.  Definiteness and the Processing of Noun Phrases in Natural Discourse , 1990, J. Semant..

[7]  Claire Gardent,et al.  Création d'un corpus annoté pour le traitement des descriptions définies , 2005 .

[8]  Mirella Lapata,et al.  Modeling Local Coherence: An Entity-Based Approach , 2005, ACL.

[9]  Mark Steedman,et al.  An Annotation Scheme for Information Status in Dialogue , 2004, LREC.

[10]  Pedro M. Domingos,et al.  Markov Logic: An Interface Layer for Artificial Intelligence , 2009, Markov Logic: An Interface Layer for Artificial Intelligence.

[11]  Roser Morante,et al.  SemEval-2010 Task 10: Linking Events and Their Participants in Discourse , 2009, SemEval@ACL.

[12]  Tommaso Caselli,et al.  Annotating Bridging Anaphors in Italian: in Search of Reliability , 2006, LREC.

[13]  Hwee Tou Ng,et al.  A Machine Learning Approach to Coreference Resolution of Noun Phrases , 2001, CL.

[14]  Sebastian Riedel.  Improving the Accuracy and Efficiency of MAP Inference for Markov Logic , 2008, UAI.

[15]  Malvina Nissim,et al.  Using the Web in Machine Learning for Other-Anaphora Resolution , 2003, EMNLP.

[16]  Nina Seemann,et al.  A Recursive Annotation Scheme for Referential Information Status , 2010, LREC.

[17]  Iván V. Meza,et al.  Jointly Identifying Predicates, Arguments and Senses using Markov Logic , 2009, NAACL.

[18]  Michael Strube,et al.  Jointly Disambiguating and Clustering Concepts and Entities with Markov Logic , 2012, COLING.

[19]  Massimo Poesio,et al.  Associative Descriptions and Salience: A Preliminary Investigation , 2003 .

[20]  Matthias Buch-Kromann,et al.  Anaphoric Relations in the Copenhagen Dependency Treebanks , 2011 .

[21]  Vincent Ng,et al.  Learning the Fine-Grained Information Status of Discourse Entities , 2012, EACL.

[22]  Ellen F. Prince,et al.  Toward a taxonomy of given-new information , 1981 .

[23]  Massimo Poesio,et al.  Learning to Resolve Bridging References , 2004, ACL.

[24]  Malvina Nissim,et al.  Using the Web for Nominal Anaphora Resolution , 2003 .

[25]  Aoife Cahill,et al.  Automatically Acquiring Fine-Grained Information Status Distinctions in German , 2012, SIGDIAL Conference.

[26]  Michael Strube,et al.  Collective Classification for Fine-grained Information Status , 2012, ACL.

[27]  Joyce Yue Chai,et al.  Semantic Role Labeling of Implicit Arguments for Nominal Predicates , 2012, CL.

[28]  Renata Vieira,et al.  A Corpus-based Investigation of Definite Description Use , 1997, CL.

[29]  Treebank Penn,et al.  Linguistic Data Consortium , 1999 .

[30]  Herbert H. Clark,et al.  Bridging , 1975, TINLAP.

[31]  Pedro M. Domingos,et al.  Joint Unsupervised Coreference Resolution with Markov Logic , 2008, EMNLP.

[32]  Jeanette K. Gundel,et al.  Cognitive Status and the Form of Referring Expressions in Discourse , 1993 .