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Balancing Speed and Accuracy in Neural-Enhanced Phonetic Name Matching

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track (ECML PKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12979))

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Abstract

Automatic co-text free name matching has a variety of important real-world applications, ranging from fiscal compliance to border control. Name matching systems use a variety of engines to compare two names for similarity, with one of the most critical being phonetic name similarity. In this work, we re-frame existing work on neural sequence-to-sequence transliteration such that it can be applied to name matching. Subsequently, for performance reasons, we then build upon this work to utilize an alternative, non-recurrent neural encoder module. This ultimately yields a model which is 63% faster while still maintaining a 16% improvement in averaged precision over our baseline model.

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References

  1. Al-Hagree, S., Al-Sanabani, M., Alalayah, K.M., Hadwan, M.: Designing an accurate and efficient algorithm for matching Arabic names. In: 2019 First International Conference of Intelligent Computing and Engineering (ICOICE), pp. 1–12 (2019).https://doi.org/10.1109/ICOICE48418.2019.9035184

  2. Al-Hagree, S., Al-Sanabani, M., Hadwan, M., Al-Hagery, M.A.: An improved n-gram distance for names matching. In: 2019 First International Conference of Intelligent Computing and Engineering (ICOICE), pp. 1–7 (2019). https://doi.org/10.1109/ICOICE48418.2019.9035154

  3. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May, 2015, Conference Track Proceedings (2015), http://arxiv.org/abs/1409.0473

  4. Baum, L.E., Petrie, T.: Statistical inference for probabilistic functions of finite state Markov chains. Ann. Math. Statist. 37(6), 1554–1563 (1966). https://doi.org/10.1214/aoms/1177699147

  5. Belinkov, Y., Durrani, N., Dalvi, F., Sajjad, H., Glass, J.: What do neural machine translation models learn about morphology? In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 861–872. Association for Computational Linguistics, Vancouver, Canada, July 2017. https://doi.org/10.18653/v1/P17-1080. https://www.aclweb.org/anthology/P17-1080

  6. Chen, Y., Skiena, S.: False-friend detection and entity matching via unsupervised transliteration. CoRR abs/1611.06722 (2016). http://arxiv.org/abs/1611.06722

  7. Cun, Y.L., et al.: Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems 2, pp. 396–404. Morgan Kaufmann Publishers Inc., San Francisco (1990)

    Google Scholar 

  8. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Stat. Soc. Series B (Methodological) 39(1), 1–38 (1977), http://www.jstor.org/stable/2984875

  9. Dhore, M., Shantanu, K., Sonwalkar, T.: Hindi to English machine transliteration of named entities using conditional random fields. Int. J. Comput. Appl. 48, July 2012. https://doi.org/10.5120/7522-0624

  10. Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G.J.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge (1998)

    Google Scholar 

  11. Ebraheem, M., Thirumuruganathan, S., Joty, S., Ouzzani, M., Tang, N.: Distributed representations of tuples for entity resolution. Proc. VLDB Endow. 11(11), 1454–1467 (2018). https://doi.org/10.14778/3236187.3236198

  12. Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4), 193–202 (1980).https://doi.org/10.1007/bf00344251, https://doi.org/10.14778/3236187.3236198

  13. Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. In: Proceedings of the 34th International Conference on Machine Learning - Volume 70, ICML 2017, pp. 1243–1252. JMLR.org (2017)

    Google Scholar 

  14. Gong, J., Newman, B.: English-Chinese name machine transliteration using search and neural network models (2018)

    Google Scholar 

  15. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Google Scholar 

  16. ISO: ISO Standard 646, 7-Bit Coded Character Set for Information Processing Interchange. International Organization for Standardization, second edn. (1983). http://www.iso.ch/cate/d4777.html, also available as ECMA-6

  17. Kim, Y., Jernite, Y., Sontag, D., Rush, A.M.: Character-aware neural language models. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 2741–2749. AAAI Press (2016)

    Google Scholar 

  18. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May, 2015, Conference Track Proceedings (2015). http://arxiv.org/abs/1412.6980

  19. Kolitsas, N., Ganea, O.E., Hofmann, T.: End-to-end neural entity linking. In: Proceedings of the 22nd Conference on Computational Natural Language Learning. pp. 519–529. Association for Computational Linguistics, Brussels, Belgium, October 2018. https://doi.org/10.18653/v1/K18-1050, https://www.aclweb.org/anthology/K18-1050

  20. Lee, C., Cheon, J., Kim, J., Kim, T., Kang, I.: Verification of transliteration pairs using distance LSTM-CNN with layer normalization. In: Annual Conference on Human and Language Technology, pp. 76–81. Human and Language Technology (2017)

    Google Scholar 

  21. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10, 707 (1966)

    MathSciNet  Google Scholar 

  22. Li, T., Zhao, T., Finch, A., Zhang, C.: A tightly-coupled unsupervised clustering and bilingual alignment model for transliteration. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 393–398. Association for Computational Linguistics, Sofia, Bulgaria, August 2013. https://www.aclweb.org/anthology/P13-2070

  23. Li, Y., Li, J., Suhara, Y., Doan, A., Tan, W.C.: Deep entity matching with pre-trained language models. Proc. VLDB Endow. 14(1), 50–60 (2020). https://doi.org/10.14778/3421424.3421431. https://doi.org/10.14778/3421424.3421431

  24. Martins, P.H., Marinho, Z., Martins, A.F.T.: Joint learning of named entity recognition and entity linking. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pp. 190–196. Association for Computational Linguistics, Florence, Italy, July 2019. https://doi.org/10.18653/v1/P19-2026, https://www.aclweb.org/anthology/P19-2026

  25. Medhat, D., Hassan, A., Salama, C.: A hybrid cross-language name matching technique using novel modified Levenshtein distance. In: 2015 Tenth International Conference on Computer Engineering Systems (ICCES), pp. 204–209 (2015). https://doi.org/10.1109/ICCES.2015.7393046

  26. Merhav, Y., Ash, S.: Design challenges in named entity transliteration. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 630–640 (2018)

    Google Scholar 

  27. Nabende, P., Tiedemann, J., Nerbonne, J.: Pair hidden Markov model for named entity matching. In: Sobh, T. (ed.) Innovations and Advances in Computer Sciences and Engineering, pp. 497–502. Springer, Netherlands (2010)

    Chapter  Google Scholar 

  28. Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML (2010)

    Google Scholar 

  29. Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. In: Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, pp. 841–848. MIT Press, Cambridge (2001)

    Google Scholar 

  30. Nie, H., et al.: Deep sequence-to-sequence entity matching for heterogeneous entity resolution. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, pp. 629–638. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3357384.3358018

  31. Peled, O., Fire, M., Rokach, L., Elovici, Y.: Matching entities across online social networks. Neurocomputing 210, 91–106 (2016)

    Google Scholar 

  32. Priyadarshani, H., Rajapaksha, M., Ranasinghe, M., Sarveswaran, K., Dias, G.: Statistical machine learning for transliteration: Transliterating names between Sinhala, Tamil and English. In: 2019 International Conference on Asian Language Processing (IALP), pp. 244–249 (2019). https://doi.org/10.1109/IALP48816.2019.9037651

  33. Qu, W.: English-Chinese name transliteration by latent analogy. In: Proceedings of the 2013 International Conference on Computational and Information Sciences, ICCIS 2013, pp. 575–578. IEEE Computer Society, USA (2013). https://doi.org/10.1109/ICCIS.2013.159

  34. Rosca, M., Breuel, T.: Sequence-to-sequence neural network models for transliteration. arXiv preprint arXiv:1610.09565 (2016)

  35. Russell, R.C.: Index (April 1918), US Patent 1,261,167

    Google Scholar 

  36. Sarkar, K., Chatterjee, S.: Bengali-to-english forward and backward machine transliteration using support vector machines. In: Mandal, J.K., Dutta, P., Mukhopadhyay, S. (eds.) CICBA 2017. CCIS, vol. 776, pp. 552–566. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-6430-2_43

    Chapter  Google Scholar 

  37. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2, NIPS 2014, pp. 3104–3112. MIT Press, Cambridge (2014)

    Google Scholar 

  38. Upadhyay, S., Kodner, J., Roth, D.: Bootstrapping transliteration with constrained discovery for low-resource languages. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 501–511. Association for Computational Linguistics, Brussels, Belgium, October–November 2018. https://doi.org/10.18653/v1/D18-1046, https://www.aclweb.org/anthology/D18-1046

  39. Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 6000–6010. Curran Associates Inc., Red Hook (2017)

    Google Scholar 

  40. Wang, D., Xu, J., Chen, Y., Zhang, Y.: Monolingual corpora based Japanese-Chinese translation extraction for kana names. J. Chinese Inf. Process. 29(5), 11 (2015)

    Google Scholar 

  41. Wu, L., Petroni, F., Josifoski, M., Riedel, S., Zettlemoyer, L.: Scalable zero-shot entity linking with dense entity retrieval. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6397–6407. Association for Computational Linguistics, Online, November 2020. https://doi.org/10.18653/v1/2020.emnlp-main.519

  42. Wu, Y., et al.: Google’s neural machine translation system: Bridging the gap between human and machine translation. CoRR abs/1609.08144 (2016). http://arxiv.org/abs/1609.08144

  43. Yamani, Z., Nurmaini, S., Firdaus, R, M.N., Sari, W.K.: Author matching using string similarities and deep neural networks. In: Proceedings of the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019), pp. 474–479. Atlantis Press (2020). https://doi.org/10.2991/aisr.k.200424.073

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Authors and Affiliations

  1. Basis Technology, 1060 Broadway, Somerville, MA, 02144, USA

    Philip Blair, Carmel Eliav, Fiona Hasanaj & Kfir Bar

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  1. Philip Blair
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  2. Carmel Eliav
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Correspondence to Philip Blair .

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Editors and Affiliations

  1. Facebook AI, Seattle, WA, USA

    Yuxiao Dong

  2. Torre Telefonica, Barcelona, Spain

    Nicolas Kourtellis

  3. Bielefeld University, CITEC, Bielefeld, Germany

    Barbara Hammer

  4. Basque Center for Applied Mathematics, Bilbao, Spain

    Jose A. Lozano

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Blair, P., Eliav, C., Hasanaj, F., Bar, K. (2021). Balancing Speed and Accuracy in Neural-Enhanced Phonetic Name Matching. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12979. Springer, Cham. https://doi.org/10.1007/978-3-030-86517-7_17

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