A Joint Inference Architecture for Global Coreference Clustering with Anaphoricity

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8105)


We present an architecture for coreference resolution based on joint inference over anaphoricity and coreference, using Markov Logic Networks. Mentions are discriminatively clustered with discourse entities established by an anaphoricity classifier. Our entity-based coreference architecture is realized in a joint inference setting to compensate for erroneous anaphoricity classifications and avoids local coreference misclassifications through global consistency constraints. Defining pairwise coreference features in a global setting achieves an efficient entity-based perspective. With a small feature set we obtain a performance of 63.56% (gold mentions) on the official CoNLL 2012 data set.


Hard Constraint Share Task Markov Network Rule Weight Rule Schema 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bengtson, E., Roth, D.: Understanding the value of features for coreference resolution. In: Proceedings of EMNLP 2008, pp. 294–303 (2008)Google Scholar
  2. Denis, P., Baldridge, J.: Specialized models and ranking for coreference resolution. In: Proceedings of EMNLP 2008, pp. 660–669 (2008)Google Scholar
  3. Denis, P., Baldridge, J.: Global joint models for coreference resolution and named entity classification. Procesamiento del Lenguaje Natural 42(1), 87–96 (2009)Google Scholar
  4. Hall, M., Eibe, F., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA Data Mining Software: An Update. ACM SIGKDD Explorations Newsletter 11(1), 10–18 (2009)CrossRefGoogle Scholar
  5. Lowd, D., Domingos, P.: Efficient weight learning for markov logic networks. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 200–211. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. Luo, X., Ittycheriah, A., Jing, H.: A mention-synchronous coreference resolution algorithm based on the bell tree. In: Proceedings of ACL 2004 (2004)Google Scholar
  7. Martschat, S., Cai, J., Broscheit, S., Mújdricza-Maydt, E., Strube, M.: A Multigraph Model for Coreference Resolution. In: Proceedings of EMNLP-CoNLL 2012: Shared Task, pp. 100–106 (2012)Google Scholar
  8. Ng, V., Cardie, C.: Improving machine learning approaches to coreference resolution. In: Proceedings of ACL 2002, pp. 104–111 (2002)Google Scholar
  9. Niu, F., Ré, C., Doan, A., Shavlik, J.: Tuffy: scaling up statistical inference in Markov logic networks using an RDBMS. Proceedings of the VLDB Endowment 4(6), 373–384 (2011)Google Scholar
  10. Poesio, M., Alexandrov-Kabadjov, M., Vieria, R., Goulart, R., Uryupina, O.: Does discourse-new detection help definite description resolution? In: Proceedings of IWCS, vol. 6, pp. 236–246 (2005)Google Scholar
  11. Poon, H., Domingos, P.: Joint unsupervised coreference resolution with Markov logic. In: Proceedings of EMNLP 2008, pp. 650–659 (2008)Google Scholar
  12. Pradhan, S., Moschitti, A., Xue, N.: CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes. In: Proceedings of EMNLP-CoNLL: Shared Task, pp. 1–27 (2012)Google Scholar
  13. Rahman, A., Ng, V.: Narrowing the modeling gap: a cluster-ranking approach to coreference resolution. Journal of Artificial Intelligence Research 40(1), 469–521 (2011)Google Scholar
  14. Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1), 107–136 (2006)CrossRefGoogle Scholar
  15. Ritz, J.: Using tf-idf-related Measures for Determining the Anaphoricity of Noun Phrases. In: Proceedings of KONVENS 2010, pp. 85–92 (2010)Google Scholar
  16. Song, Y., Jiang, J., Zhao, X., Li, S., Wang, H.: Joint Learning for Coreference Resolution with Markov Logic. In: Proceedings of EMNLP-CoNLL 2012, pp. 1245–1254 (2012)Google Scholar
  17. Soon, W., Ng, H., Li, D.: A machine learning approach to coreference resolution of noun phrases. Computational Linguistics 27(4), 521–544 (2001)CrossRefGoogle Scholar
  18. Wellner, B., McCallum, A., Peng, F., Hay, M.: An integrated, conditional model of information extraction and coreference with application to citation matching. In: Proceedings of UAI 2004, pp. 593–601 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computational LinguisticsHeidelberg UniversityGermany

Personalised recommendations