Abstract
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.
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References
Bengtson, E., Roth, D.: Understanding the value of features for coreference resolution. In: Proceedings of EMNLP 2008, pp. 294–303 (2008)
Denis, P., Baldridge, J.: Specialized models and ranking for coreference resolution. In: Proceedings of EMNLP 2008, pp. 660–669 (2008)
Denis, P., Baldridge, J.: Global joint models for coreference resolution and named entity classification. Procesamiento del Lenguaje Natural 42(1), 87–96 (2009)
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)
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)
Luo, X., Ittycheriah, A., Jing, H.: A mention-synchronous coreference resolution algorithm based on the bell tree. In: Proceedings of ACL 2004 (2004)
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)
Ng, V., Cardie, C.: Improving machine learning approaches to coreference resolution. In: Proceedings of ACL 2002, pp. 104–111 (2002)
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)
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)
Poon, H., Domingos, P.: Joint unsupervised coreference resolution with Markov logic. In: Proceedings of EMNLP 2008, pp. 650–659 (2008)
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)
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)
Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62(1), 107–136 (2006)
Ritz, J.: Using tf-idf-related Measures for Determining the Anaphoricity of Noun Phrases. In: Proceedings of KONVENS 2010, pp. 85–92 (2010)
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)
Soon, W., Ng, H., Li, D.: A machine learning approach to coreference resolution of noun phrases. Computational Linguistics 27(4), 521–544 (2001)
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)
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Bögel, T., Frank, A. (2013). A Joint Inference Architecture for Global Coreference Clustering with Anaphoricity. In: Gurevych, I., Biemann, C., Zesch, T. (eds) Language Processing and Knowledge in the Web. Lecture Notes in Computer Science(), vol 8105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40722-2_4
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DOI: https://doi.org/10.1007/978-3-642-40722-2_4
Publisher Name: Springer, Berlin, Heidelberg
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