Home  ›  Acquisition of Clitics the State of the Art Tsakali 2014

Acquisition of Clitics the State of the Art Tsakali 2014

Written By Wednesday, 27 April 2022 Add Comment Edit

Abstract

The paper describes a new Text Preprocessing Pipeline based on a Hybrid approach which involve rule-based and stochastic approaches. The presented pipeline is part of a larger project titled Large Data for Multi-Amanuensis Specialized System developed by Network Contacts in collaboration with University of Salerno and other institutional partners. The aim of the project is to build an Hybrid Question Answering Organization composed by sets of Dialog Bots able to process great volumes of information. Due to the importance of unstructured textual data, a particular focus of the project is on automatic processing of Text. The newspaper will describe the three main modules of the preprocessing pipeline, which involve a Style Correction Module, a Clitic Decomposition Module and a POS Tagging and Lemmatization Module.

Notes

  1. 1.

    Network Contacts is one of the major national thespian in BPO services (Business organization Process Outsourcing), CRM (Client Relationship Management), Digital Interaction and Phone call & Contact Eye.

  2. 2.

    With MWE nosotros make reference both to lemmatized MWE, e.g. carta di credito, which are listed in the Noesis base, and to a fix of MWE which are domain specific, e.g. all inclusive unlimited, for the domain of telecommunications.

References

  1. Amato, F., Colace, F., Greco, L., Moscato, V., Picariello, A.: Semantic processing of multimedia information for due east-government applications. J. Vis. Lang. Comput. 32, 35–41 (2016). https://www.scopus.com/in/record.uri?eid=ii-s2.0-84949036166&doi=10.1016%2fj.jvlc.2015.10.012&partnerID=40&md5=77d8fbfd4293af91d382e770553e159c.

  2. Amato, F., Mazzeo, A., Penta, A., Picariello, A.: Noesis representation and management for due east-government documents. In: IFIP International Federation for Information Processing, vol. 280, pp. 31–40 (2008). https://www.scopus.com/inward/record.uri?eid=ii-s2.0-47249122655&doi=10.1007%2f978-0-387-09712-1_4&partnerID=40&md5=d8e43aeb328d1fdcb45aa323689f7010.

  3. Amato, F., Moscato, F.: A model driven approach to data privacy verification in e-health systems. Trans. Data Privacy eight(3), 273–296 (2015). https://www.scopus.com/inward/record.uri?eid=two-s2.0-84949775424&partnerID=40&md5=2ca02581d8482fbc2f8489a423a80290.

    Google Scholar

  4. Attardi, M., Fuschetto, A., Tamberi, F., Simi, M., Vecchi, E.M.: Experiments in tagger combination: arbitrating, guessing, correcting, suggesting. In: Proceedings of Workshop Evalita, p. 10 (2009)

    Google Scholar

  5. Bahdanau, D., Cho, Chiliad., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)

  6. Choi, J.D.: Dynamic feature induction: the last gist to the country-of-the-art. In: Proceedings of NAACL-HLT, pp. 271–281 (2016)

    Google Scholar

  7. Chrupała, One thousand., Dinu, Grand., Van Genabith, J.: Learning morphology with Morfette (2008)

    Google Scholar

  8. Collins, 1000.: Discriminative preparation methods for hidden Markov models: theory and experiments with perceptron algorithms. In: Proceedings of the ACL-02 Conference on Empirical Methods in Tongue Processing, vol. x, pp. 1–8. Association for Computational Linguistics (2002)

    Google Scholar

  9. Constant, M., Tellier, I., Duchier, D., Dupont, Y., Sigogne, A., Billot, S.: Intégrer des connaissances linguistiques dans united nations crf: application à 50'apprentissage d'united nations segmenteur-étiqueteur du français. In: TALN, vol. 1, p. 321 (2011)

    Google Scholar

  10. Dahlmeier, D., Ng, H.T.: Better evaluation for grammatical mistake correction. In: Proceedings of the 2012 Briefing of the North American Chapter of the Association for Computational Linguistics: Man Language Technologies, pp. 568–572. Association for Computational Linguistics (2012)

    Google Scholar

  11. Dell'Orletta, F.: Ensemble system for part-of-speech tagging. In: Proceedings of EVALITA, vol. 9, pp. 1–8 (2009)

    Google Scholar

  12. Delmonte, R.: Generating and parsing clitics with getarun. In: CLIN (1999)

    Google Scholar

  13. Favretti, R.R., Tamburini, F., De Santis, C.: CORIS/CODIS: a corpus of written Italian based on a divers and a dynamic model. A Rainbow of Corpora: Corpus Linguistics and the Languages of the World. Munich: Lincom-Europa (2002)

    Google Scholar

  14. Felice, Thousand., Yuan, Z., Andersen, Ø.Eastward., Yannakoudakis, H., Kochmar, E.: Grammatical error correction using hybrid systems and blazon filtering. In: Proceedings of the Eighteenth Conference on Computational Natural Linguistic communication Learning: Shared Job, pp. fifteen–24 (2014)

    Google Scholar

  15. Giménez, J., Marquez, Fifty.: Svmtool: a general POS tagger generator based on support vector machines. In: Proceedings of the 4th International Briefing on Language Resources and Evaluation. Citeseer (2004)

    Google Scholar

  16. Han, North.R., Chodorow, M., Leacock, C.: Detecting errors in English language article usage with a maximum entropy classifier trained on a large, diverse corpus. In: LREC (2004)

    Google Scholar

  17. Huang, Z., Xu, W., Yu, Grand.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)

  18. Ingason, A.1000., Helgadóttir, S., Loftsson, H., Rögnvaldsson, E.: A mixed method lemmatization algorithm using a hierarchy of linguistic identities (HOLI). In: Advances in Natural Language Processing, pp. 205–216. Springer, Heidelberg (2008)

    Google Scholar

  19. Junczys-Dowmunt, M., Grundkiewicz, R.: The AMU system in the CoNLL-2014 shared task: grammatical mistake correction past data-intensive and feature-rich statistical automobile translation. In: Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task, pp. 25–33 (2014)

    Google Scholar

  20. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Soviet Phys. doklady ten, 707–710 (1966)

    MathSciNet  Google Scholar

  21. Lyding, V., Stemle, E., Borghetti, C., Brunello, Chiliad., Castagnoli, S., Dell'Orletta, F., Dittmann, H., Lenci, A., Pirrelli, V.: The PAISA corpus of Italian spider web texts. In: Proceedings of the ninth Web as Corpus Workshop (WaC-nine), pp. 36–43 (2014)

    Google Scholar

  22. Manning, C.D.: Part-of-speech communication tagging from 97% to 100%: is it time for some linguistics? In: International Conference on Intelligent Text Processing and Computational Linguistics, pp. 171–189. Springer, Heidelberg (2011)

    Google Scholar

  23. Mizumoto, T., Hayashibe, Y., Komachi, M., Nagata, M., Matsumoto, Y.: The effect of learner corpus size in grammatical error correction of ESL writings. In: Proceedings of COLING 2012: Posters, pp. 863–872 (2012)

    Google Scholar

  24. Monachesi, P.: A grammar of Italian clitics (1996)

    Google Scholar

  25. Monachesi, P.: A lexical approach to Italian cliticization (1999)

    Google Scholar

  26. Palmero Aprosio, A., Moretti, One thousand.: Italy goes to Stanford: a drove of CoreNLP modules for Italian. ArXiv east-prints (2016)

    Google Scholar

  27. Pianta, E., Zanoli, R.: TagPro: a organisation for Italian PoS tagging based on SVM. Intelligenza Artificiale 4(two), 8–9 (2007)

    Google Scholar

  28. Porter, Grand.F.: An algorithm for suffix stripping. Plan 14(iii), 130–137 (1980)

    CrossRef  Google Scholar

  29. Rozovskaya, A., Roth, D.: Algorithm selection and model adaptation for ESL correction tasks. In: Proceedings of the 49th Annual Coming together of the Clan for Computational Linguistics: Human being Language Technologies, vol. ane, pp. 924–933. Clan for Computational Linguistics (2011)

    Google Scholar

  30. Russi, C.: Italian clitics: an empirical study, vol. 193. Walter de Gruyter (2008)

    Google Scholar

  31. Schmid, H.: Treetagger—a language independent part-of-oral communication tagger. Institut für Maschinelle Sprachverarbeitung, Universität Stuttgart 43, 28 (1995)

    Google Scholar

  32. Shen, L., Satta, G., Joshi, A.: Guided learning for bidirectional sequence classification. In: ACL, vol. vii, pp. 760–767. Citeseer (2007)

    Google Scholar

  33. Smedt, T.D., Daelemans, W.: Pattern for python. J. Mach. Learn. Res. 13(Jun), 2063–2067 (2012)

    Google Scholar

  34. Srinivasan, A., Compton, P., Malor, R., Edwards, G., Lazarus, L.: Noesis acquisition in context for a complex domain. Pre-print of Proceedings of the Fifth EKAW91 (1991)

    Google Scholar

  35. Sun, C., Jin, X., Lin, L., Zhao, Y., Wang, X.: Convolutional neural networks for correcting English article errors. In: Natural Language Processing and Chinese Computing, pp. 102–110. Springer, Heidelberg (2015)

    Google Scholar

  36. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Data Processing Systems, pp. 3104–3112 (2014)

    Google Scholar

  37. Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech communication tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 173–180. Association for Computational Linguistics (2003)

    Google Scholar

  38. Vietri, Due south.: Dizionari elettronici e grammatiche a stati finiti: metodi di analisi formale della lingua italiana. Plectica (2008)

    Google Scholar

  39. Vietri, S.: The Italian module for NooJ. In: Proceedings of the Start Italian Conference on Computational Linguistics, CLiC-it, pp. 389–393 (2014)

    Google Scholar

Download references

Writer information

Affiliations

  1. Università di Salerno, Via Giovanni Paolo Ii, 84084, Fisciano, SA, Italy

    Alessandro Maisto, Serena Pelosi & Michele Stingo

  2. Network Contacts, Via Olivetti north°17, 70056, Molfetta, BA, Italian republic

    Massimiliano Polito

Respective writer

Correspondence to Alessandro Maisto .

Copyright data

© 2019 Springer Nature Switzerland AG

Nigh this paper

Verify currency and authenticity via CrossMark

Cite this paper

Maisto, A., Pelosi, S., Polito, G., Stingo, M. (2019). Automatic Text Preprocessing for Intelligent Dialog Agents. In: Barolli, L., Takizawa, K., Xhafa, F., Enokido, T. (eds) Web, Bogus Intelligence and Network Applications. WAINA 2019. Advances in Intelligent Systems and Computing, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-15035-8_78

Download citation

  • .RIS
  • .ENW
  • .BIB

  • DOI : https://doi.org/x.1007/978-3-030-15035-8_78

  • Published:

  • Publisher Proper noun: Springer, Cham

  • Print ISBN: 978-iii-030-15034-ane

  • Online ISBN: 978-three-030-15035-8

  • eBook Packages: Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

coopersciusurturs.blogspot.com

Source: https://link.springer.com/chapter/10.1007/978-3-030-15035-8_78

0 Response to "Acquisition of Clitics the State of the Art Tsakali 2014"

Post a Comment

Subscribe to: Post Comments (Atom)

Iklan Atas Artikel

Iklan Tengah Artikel 1

Iklan Tengah Artikel 2

Iklan Bawah Artikel