- (Wick et al., 2009) ⇒ Michael Wick, Aron Culotta, Khashayar Rohanimanesh, Andrew McCallum. (2009). “An Entity Based Model for Coreference Resolution.” In: Proceedings of the SIAM International Conference on Data Mining (SDM 2009).
- It describes it as a Statistical Modeling Algorithm that applies a Discriminatively Trained Undirected Graphical Models to the Citation Matching Task.
Recently, many advanced machine learning approaches have been proposed for coreference resolution; however, all of the discriminatively-trained models reason over mentions, rather than entities. That is, they do not explicitly contain variables indicating the “canonical” values for each attribute of an entity (e.g., name, venue, title, etc.). This canonicalization step is typically implemented as a post-processing routine to coreference resolution prior to adding the extracted entity to a database. In this paper, we propose a discriminatively-trained model that jointly performs coreference resolution and canonicalization, enabling features over hypothesized entities. We validate our approach on two different coreference problems: newswire anaphora resolution and research paper citation matching, demonstrating improvements in both tasks and achieving an error reduction of up to 62% when compared to a method that reasons about mentions only.
-  W. M. Soon, H. T. Ng, and D. C. Y. Lim, “A machine learning approach to coreference resolution of noun phrases,” Comput. Linguist., vol. 27, no. 4, pp. 521–544, 2001.
-  V. Ng and C. Cardie, “Improving machine learning approaches to coreference resolution,” in ACL, 2002.
-  Andrew McCallum and B.Wellner, “Toward conditional models of identity uncertainty with application to proper noun coreference,” in IJCAI Workshop on Information Integration on the Web, 2003.
-  Parag and Pedro Domingos, “Multi-relational record linkage,” In: Proceedings of the KDD-2004 Workshop on Multi-Relational Data Mining, Aug. 2004, pp. 31–48.
-  (Pasula et al., 2003) ⇒ Hanna Pasula, Bhaskara Marthi, Brian Milch, Stuart Russell, and Ilya Shpitser. (2003). “Identity Uncertainty and Citation Matching.” In: Proceedings of Advances in Neural Information Processing, 15 (NIPS 2003).
-  H. Daumé III and D. Marcu, “A large-scale exploration of effective global features for a joint entity detection and tracking model,” in HLT/EMNLP, Vancouver, Canada, 2005.
-  A. Culotta, M. Wick, and Andrew McCallum, “First-order probabilistic models for coreference resolution,” in HLT/NAACL, 2007.
-  A. Culotta, M.Wick, R. Hall, M. Marzilli, and Andrew McCallum, “Canonicalization of database records using adaptive similarity measures,” In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Jose, CA, 2007.
-  V. Levenshtein, “Binary codes capable of correcting deletions, insertions and reversals.” Doklady Akademii Nauk SSR, vol. 163, no. 4, pp. 845–848, 1965.
-  John D. Lafferty, Andrew McCallum, and Fernando Pereira, “Conditional random fields: Probabilistic models for segmenting and labeling sequence data,” in ICML. Morgan Kaufmann, San Francisco, CA, 2001, pp. 282–289.
-  C. Sutton and Andrew McCallum, “An introduction to conditional random fields for relational learning,” in Introduction to Statistical Relational Learning, Lise Getoor and Ben Taskar, Eds. MIT Press, 2007, pp. 93–127.
-  C. Nicolae and G. Nicolae, “Bestcut: A graph algorithm for coreference resolution,” in EMNLP. Sydney, Australia: Association for Computational Linguistics, July 2006, pp. 275–283.
-  (Poon & Domingos) ⇒ H. Poon and Pedro Domingos. (2007). “Joint inference in information extraction.” In: Proceedings of the Twenty-Second National Conference on Artificial Intelligence (AAAI 2007).
-  B. Amit and B. Baldwin, “Algorithms for scoring coreference chains,” In: Proceedings of MUC7, 1998.
-  M. Vilain, J. Burger, J. Aberdeen, D. Connolly, and Lynette Hirschman, “A model-theoretic coreference scoring scheme,” In: Proceedings of MUC6, 1995, pp. 45–52.
-  V. Ng, “Machine learning for coreference resolution: From local classification to global ranking,” in ACL, 2005.
-  J. Artiles, Satoshi Sekine, and J. Gonzalo, “Web people search: results of the first evaluation and the plan for the second,” in WWW ’08: Proceeding of the 17th International Conference on World Wide Web. New York, NY, USA: ACM, 2008, pp. 1071–1072.
-  J. F. McCarthy and W. G. Lehnert, “Using decision trees for coreference resolution,” in IJCAI, 1995, pp. 1050–1055.
-  A. Culotta, P. Kanani, R. Hall, M. Wick, and Andrew McCallum, “Author disambiguation using error-driven machine learning with a ranking loss function,” in Sixth International Workshop on Information Integration on the Web (IIWeb- 07), Vancouver, Canada, (2007). [Online]. Available: http://www.cs.umass.edu/ culotta/pubs/culotta07author.pdf
-  Andrew McCallum and B.Wellner, “Conditional models of identity uncertainty with application to noun coreference,” in NIPS17, L. K. Saul, Yair Weiss, and L. Bottou, Eds. Cambridge, MA: MIT Press, 2005.
-  M. Poesio, D. Day, R. Arstein, J. Duncan, V. Eidelman, C. Giuliano, R. Hall, J. Hitzeman, A. Jern, M. Kabadjov, G. Mann, P. McNamee, Alessandro Moschitti, S. Ponzetto, J. Smith, J. Steinberger, M. Strube, J. Su, Y. Versley, X. Yang, and M. Wick, “Exploiting encyclypedic and lexical resources for entity disambiguation,” Johns Hopkings University, Baltimore, Tech. Rep., 2007.
-  Y. Versley, “Antecedent selection techniques for high-recall coreference resolution,” in Emperical Methods in Natural Language Processing EMNLP-CoNLL, 2007.
-  A. Haghighi and D. Klein, “Unsupervised coreference resolution in a nonparametric bayesian model,” in ACL, 2007.
-  H. B. Newcombe, J. M. Kennedy, S. Axford, and A. James, “Automatic linkage of vital records,” Science, vol. 130, pp. 954–959, 1959.
-  H. B. Newcombe and J. M. Kennedy, “Record linkage: Making maximum use of the discriminating power of identifying information,” Comm. ACM, vol. 5, pp. 563–566, 1962.
-  H. B. Newcombe, “Record linkage: The design of efficient systems for linking records into individual and family histories,” Am. J. Human Genetics, vol. 19, pp. 335–359, 1967.
-  B. Milch, B. Marthi, S. Russell, D. Sontag, D. L. Ong, and A. Kolobov, “BLOG: Probabilistic models with unknown objects,” in IJCAI, 2005.
-  Aron Culottaand Andrew McCallum, “Practical markov logic containing first-order quantifiers with application to identity uncertainty,” University of Massachusetts, Tech. Rep. IR-430, 2005.
-  Aron Culottaand Andrew McCallum, “Practical markov logic containing first-order quantifiers with application to identity uncertainty,” in HLT Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing, 2006.
-  R. Hall, C. Sutton, and Andrew McCallum, “Unsupervised coreference of publication venues,” University of Massachusetts, Amherst, Amherst, MA, Tech. Rep., 2007.
-  R. Hall, C. Sutton, and Andrew McCallum, “Unsupervised deduplication using cross-field dependencies,” in 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), Las Vegas, Nevada, 2008.
-  H. G. Goldberg and T. E. Senator, “Restructuring databases for knowledge discovery by consolidation and link formation,” in Knowledge Discovery and Data Mining, 1995, pp. 136–141.
-  (Wick et al., 2008) ⇒ Michael Wick, Khashayar Rohanimanesh, Karl Schultz, and Andrew McCallum. (2008). “A Unified Approach for Schema Matching, Coreference, and Canonicalization.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008).,
|Author||Michael Wick +, Aron Culotta +, Khashayar Rohanimanesh + and Andrew McCallum +|
|journal||Proceedings of the SIAM International Conference on Data Mining +|
|title||An Entity Based Model for Coreference Resolution +|