Abstract
We present a named-entity recognizer for Greek person names and temporal expressions. For temporal expressions, it relies on semi- automatically produced patterns. For person names, it employs two Support Vector Machines, that scan the input text in two passes, and active learning, which reduces the human annotation effort during training.
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References
Appelt, D., Hobbs, J., Bear, J., Israel, D., Kameyama, M., Kehler, A., Martin, D., Myers, K., Tyson, M.: SRI International FASTUS system MUC-6 test results and analysis. In: 6th Message Understanding Conference, Columbia, MD (1995)
Mitchell, B., Huyck, C., Cunningham, H., Humphreys, K., Gaizauskas, R., Azzam, S., Wilks, Y.: University of Sheffield: Description of the laSIE-II system as used for MUC-7. In: 7th Message Understanding Conference, Fairfax, VA (1998)
Bikel, D.M., Schwartz, R.L., Weischedel, R.M.: An algorithm that learns what’s in a name. Machine Learning 34(1–3), 211–231 (1999)
Zhou, G., Su, J.: Machine learning-based named entity recognition via effective integration of various evidences. Natural Language Engineering 11, 189–206 (2005)
Chieu, H.L., Ng, H.T.: Named entity recognition with a maximum entropy approach. In: 7th Conference on Computational Natural Language Learning, Edmonton, Canada, pp. 160–163 (2003)
Paliouras, G., Karkaletsis, V., Petasis, G., Spyropoulos, C.: Learning decision trees for named-entity recognition and classification. In: 14th European Conference on Artificial Intelligence, Berlin, Germany (2000)
Kazama, J., Makino, T., Ohta, Y., Tsujii, J.: Tuning Support Vector Machines for biomedical named entity recognition. In: ACL Workshop on Natural Language Processing in the Biomedical Domain, Philadelphia, PA, pp. 1–8 (2002)
Lee, K., Hwang, Y., Rim, H.: Two-phase biomedical NE recognition based on SVMs. In: ACL Workshop on Natural Language Processing in the Biomedical Domain, Philadelphia, PA (2002)
Petasis, G., Petridis, S., Paliouras, G., Karkaletsis, V., Perantonis, S., Spyropoulos, C.: Symbolic and neural learning for named-entity recognition. In: Symposium on Computational Intelligence and Learning, Chios, Greece, pp. 58–66 (2000)
Schohn, G., Cohn, D.: Less is more: Active learning with Support Vector Machines. In: 17th Int. Conference on Machine Learning, Stanford, CA, pp. 839–846 (2000)
Brinker, K.: Incorporating diversity in active learning with Support Vector Machines. In: 20th International Conference on Machine Learning, Washington, DC, pp. 59–66 (2003)
Shen, D., Zhang, J., Su, J., Zhou, G., Tan, C.L.: Multi-criteria-based active learning for named entity recognition. In: 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain, pp. 589–596 (2004)
Vlachos, A.: Active learning with Support Vector Machines. Master’s thesis, School of Informatics, University of Edinburgh (2004)
Boutsis, S., Demiros, I., Giouli, V., Liakata, M., Papageorgiou, H., Piperidis, S.: A system for recognition of named entities in Greek. In: 2nd International Conference on Natural Language Processing, Patra, Greece, pp. 424–435 (2000)
Farmakiotou, D., Karkaletsis, V., Koutsias, J., Sigletos, G., Spyropoulos, C., Stamatopoulos, P.: Rule-based named entity recognition for Greek financial texts. In: Workshop on Computational Lexicography and Multimedia Dictionaries, Patra, Greece, pp. 75–78 (2000)
Farmakiotou, D., Karkaletsis, V., Samaritakis, G., Petasis, G., Spyropoulos, C.: Named entity recognition in Greek Web pages. In: 2nd Hellenic Conference on Artificial Intelligence, companion volume, Thessaloniki, Greece, pp. 91–102 (2002)
Karkaletsis, V., Paliouras, G., Petasis, G., Manousopoulou, N., Spyropoulos, C.: Named-entity recognition from Greek and English texts. Intelligent and Robotic Systems 26, 123–135 (1999)
Petasis, G., Vichot, F., Wolinski, F., Paliouras, G., Karkaletsis, V., Spyropoulos, C.: Using machine learning to maintain rule-based named-entity recognition and classification systems. In: 39th ACL/10th EACL, Toulouse, France, pp. 426–433 (2001)
Mikheev, A., Grover, C., Moens, M.: Description of the LTG system used for MUC-7. In: 7th Message Understanding Conference, Fairfax, VA (1998)
Lucarelli, G.: Named entity recognition and categorization in Greek texts. Master’s thesis, Department of Informatics, Athens University of Economics and Business (2005), http://www.aueb.gr/users/ion/docs/lucarelli_msc_final_report.pdf
Manning, C., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)
Tong, S., Koller, D.: Support Vector Machine active learning with applications to text classification. Machine Learning Research 2, 45–66 (2002)
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Lucarelli, G., Androutsopoulos, I. (2006). A Greek Named-Entity Recognizer That Uses Support Vector Machines and Active Learning. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds) Advances in Artificial Intelligence. SETN 2006. Lecture Notes in Computer Science(), vol 3955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752912_22
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DOI: https://doi.org/10.1007/11752912_22
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