ANEAR: Automatic Named Entity Aliasing Resolution

  • Ayah Zirikly
  • Mona Diab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7934)


Identifying the different aliases used by or for an entity is emerging as a significant problem in reliable Information Extraction systems, especially with the proliferation of social media and their ever growing impact on different aspects of modern life such as politics, finance, security, etc. In this paper, we address the novel problem of Named Entity Aliasing Resolution (NEAR). We attempt to solve the NEAR problem in a language-independent setting by extracting the different aliases and variants of person named entities. We generate feature vectors for the named entities by building co-occurrence models that use different weighting schemes. The aliasing resolution process applies unsupervised machine learning techniques over the vector space models in order to produce groups of entities along with their aliases. We test our approach on two languages: Arabic and English. We study the impact of varying the level of morphological preprocessing of the words, as well as the part of speech tags surrounding the person named entities, and the named entities’ distribution in the data set. We create novel evaluation data sets for both languages. NEAR yields better overall performance in Arabic than in English for comparable amounts of data, effectively using the POS tag information to improve performance. Our approach achieves an F β = 1score of 67.85% and 70.03% for raw English and Arabic data sets, respectively.


Vector Space Model Name Entity Recognition Computational Linguistics Defense Advance Research Project Agency Entity Disambiguation 
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. 1.
    Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL 2005, pp. 363–370. Association for Computational Linguistics, Stroudsburg (2005)CrossRefGoogle Scholar
  2. 2.
    Diab, M.: Second generation tools (amira 2.0): Fast and robust tokenization, pos tagging, and base phrase chunking. In: Choukri, K., Maegaard, B., eds.: Proceedings of the Second International Conference on Arabic Language Resources and Tools. The MEDAR Consortium, Cairo (2009)Google Scholar
  3. 3.
    Benajiba, Y., Diab, M.T., Rosso, P.: Arabic named entity recognition: A feature-driven study. IEEE Transactions on Audio, Speech & Language Processing 17(5), 926–934 (2009)CrossRefGoogle Scholar
  4. 4.
    Jiang, L., Wang, J., Luo, P., An, N., Wang, M.: Towards alias detection without string similarity: an active learning based approach. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, pp. 1155–1156. ACM, New York (2012)CrossRefGoogle Scholar
  5. 5.
    Bollegala, D., Matsuo, Y., Ishizuka, M.: Automatic discovery of personal name aliases from the web. IEEE Trans. on Knowl. and Data Eng. 23(6), 831–844 (2011)CrossRefGoogle Scholar
  6. 6.
    Han, X., Zhao, J.: Structural semantic relatedness: A knowledge-based method to named entity disambiguation. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 50–59. Association for Computational Linguistics, Uppsala (2010)Google Scholar
  7. 7.
    Cucerzan, S.: Large-scale named entity disambiguation based on wikipedia data. In: Proceedings of EMNLP-CoNLL, vol. 2007, pp. 708–716 (2007)Google Scholar
  8. 8.
    Gabrilovich, E., Markovitch, S.: Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: IJCAI 2007: Proceedings of the 20th International Joint Conference on Artifical Intelligence, pp. 1606–1611. Morgan Kaufmann Publishers Inc., San Francisco (2007)Google Scholar
  9. 9.
    Bagga, A., Baldwin, B.: Entity-based cross-document coreferencing using the vector space model. In: COLING-ACL, pp. 79–85 (1998)Google Scholar
  10. 10.
    Bagga, A., Biermann, A.W.: A methodology for cross-document coreference. In: Proceedings of the Fifth Joint Conference on Information Sciences (JCIS 2000), pp. 207–210 (2000)Google Scholar
  11. 11.
    Mann, G.S., Yarowsky, D.: Unsupervised personal name disambiguation. In: Daelemans, W., Osborne, M. (eds.) Proceedings of CoNLL-2003, pp. 33–40. Edmonton, Canada (2003)Google Scholar
  12. 12.
    Bollegala, D., Matsuo, Y., Ishizuka, M.: Automatic discovery of personal name aliases from the web. IEEE Trans. Knowl. Data Eng. 23(6), 831–844 (2011)CrossRefGoogle Scholar
  13. 13.
    Hsiung, P., Moore, A., Neil, D., Schneider, J.: Alias detection in link data sets. Master’s thesis, Technical Report CMU-RI-TR-04-22 (March 2004)Google Scholar
  14. 14.
    Charton, E., Gagnon, M.: A disambiguation resource extracted from wikipedia for semantic annotation. In: LREC, pp. 3665–3671 (2012)Google Scholar
  15. 15.
    Chen, Y., Martin, J.: Towards robust unsupervised personal name disambiguation. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 190–198. Association for Computational Linguistics, Prague (2007)Google Scholar
  16. 16.
    Sutton, C., Mccallum, A.: Introduction to Conditional Random Fields for Relational Learning. MIT Press (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ayah Zirikly
    • 1
  • Mona Diab
    • 1
  1. 1.Department of Computer ScienceThe George Washington UniversityWashington DCUSA

Personalised recommendations