Skip to main content
SpringerLink
Log in

Design of Emergency Call Record Support System Applying Natural Language Processing Techniques

  • Conference paper
  • First Online:
Information and Communication Technologies of Ecuador (TIC.EC) (TICEC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1099))

Included in the following conference series:

Abstract

Currently Command and Control Centers (C2), such as the Integrated Security Service ECU 911, are managed by Computer Assisted Dispatch Systems (CADS). These systems facilitate the registration of incidents and the distribution of rescue resources. However, the information registration process does not yet have automated methods. Important data such as: name, address, reference and categories are recorded manually, which generates problems of loss of information and inefficiency, in terms of time and attention to the incident. As a solution to these problems, the design of an emergency call record support system is proposed, based on Natural Language Processing (NLP) techniques and algorithms. Taking into consideration an analysis of the processes of the ECU 911, three modules are proposed: (1) transcription of audio to text calls (ASR), (2) extraction of relevant information (NER) such as: address and references; and (3) call classification (TF-IDF/SVM) according to service and priority. Thus obtaining the design of an automated support system for CADS, which provides quality information in a timely manner.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Servicio Integrado de Seguridad ECU 9111. Servicio Integrado de Seguridad ECU911 (2019). http://www.ecu911.gob.ec/. Accessed 2 May 2019

  2. Beynon-Davies, P.: Human error and information systems failure: the case of the London ambulance service computer-aided despatch system project. Interact. Comput. 11, 699–720 (1999). https://doi.org/10.1016/S0953-5438(98)00050-2

    Article  Google Scholar 

  3. Seattle Fire Department: Surveillance Impact Report: Computer-Aided Dispatch (CAD) (2019)

    Google Scholar 

  4. Zhang, J., Zhang, M., Ren, F., et al.: Enable automated emergency responses through an agent-based computer-aided dispatch system. In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, pp. 1844–1846 (2018)

    Google Scholar 

  5. San Francisco Fire Department. Fire Department Calls for Service (2018)

    Google Scholar 

  6. Unidad de Estadística y Evaluación ECU 911 Ambato. Estadísticas mensuales de alertas recibidas, incidentes atendidos y despachos realizados por el ECU 911 Ambato (2013)

    Google Scholar 

  7. Souza, J., Botega, L.C., Santar, E., et al.: Conceptual framework to enrich situation awareness of emergency dispatchers. In: 17th International Conference, HCI International 2015, Los Angeles, CA, USA, 2–7 August 2015, Proceedings, Part II, pp. 33–44 (2015)

    Google Scholar 

  8. Blomberg, S.N., Folke, F., Ersbøll, A.K., et al.: Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Resuscitation 1–8 (2019). https://doi.org/10.1016/j.resuscitation.2019.01.015

    Article  Google Scholar 

  9. Møller, T.P., Kjærulff, T.M., Viereck, S., et al.: The difficult medical emergency call: a register-based study of predictors and outcomes. Scand. J. Trauma Resusc. Emerg. Med. 25, 1–9 (2017). https://doi.org/10.1186/s13049-017-0366-0

    Article  Google Scholar 

  10. Yeh, L.-Y., Tsaur, W.-J., Huang, H.-H.: Secure IoT-based, incentive-aware emergency personnel dispatching scheme with weighted fine-grained access control. ACM Trans. Intell. Syst. Technol. 9, 1–23 (2017). https://doi.org/10.1145/3063716

    Article  Google Scholar 

  11. Nakata, T.: Text-mining on incident reports to find knowledge on industrial safety. In: Proceedings - Annual Reliability and Maintainability Symposium (2017)

    Google Scholar 

  12. Clegg, G.R., Lyon, R.M., James, S., et al.: Dispatch-assisted CPR: where are the hold-ups during calls to emergency dispatchers? A preliminary analysis of caller-dispatcher interactions during out-of-hospital cardiac arrest using a novel call transcription technique. Resuscitation 85, 49–52 (2014). https://doi.org/10.1016/j.resuscitation.2013.08.018

    Article  Google Scholar 

  13. Riou, M., Ball, S., Williams, T.A., et al.: The linguistic and interactional factors impacting recognition and dispatch in emergency calls for out-of-hospital cardiac arrest: a mixed-method linguistic analysis study protocol. BMJ Open 7, 1–8 (2017). https://doi.org/10.1136/bmjopen-2017-016510

    Article  Google Scholar 

  14. Ashwell, T., Elam, J.R.: How accurately can the Google web speech API recognize and transcribe Japanese L2 English learners’ oral production? Jalt Call J. 13, 59–76 (2017)

    Google Scholar 

  15. Iancu, B.: Evaluating Google speech-to-text api’s performance for romanian e-learning resources. Inform. Econ. 23, 17–25 (2019). https://doi.org/10.12948/issn14531305/23.1.2019.02

    Article  Google Scholar 

  16. Stefanovic, M., Cetic, N., Kovacevic, M., et al.: Voice control system with advanced recognition. In: 2012 20th Telecommunications Forum, TELFOR 2012 – Proceedings, pp. 1601–1604 (2012)

    Google Scholar 

  17. IBM Corporation. Watson Speech to Text (2019). https://www.ibm.com/es-es/cloud/watson-speech-to-text. Accessed 2 June 2019

  18. Amazon Web Service. Amazon Transcribe: Automatic speech recognition (2019). https://aws.amazon.com/transcribe/. Accessed 2 June 2019

  19. Principi, E., Squartini, S., Bonfigli, R., et al.: An integrated system for voice command recognition and emergency detection based on audio signals. Expert Syst. Appl. 42, 5668–5683 (2015). https://doi.org/10.1016/j.eswa.2015.02.036

    Article  Google Scholar 

  20. Balcerek, J., Pawlowski, P., Dabrowski, A.: Classification of emergency phone conversations with artificial neural network. In: Signal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA, pp. 343–348 (2017)

    Google Scholar 

  21. Lee, K., Kim, J.K., Park, M.W., et al.: A situation-based dialogue classification model for emergency calls. In: 2017 International Conference on Platform Technology and Service, PlatCon 2017 – Proceedings, pp. 1–4 (2017)

    Google Scholar 

  22. Gutiérrez, R., Castillo, A., Bucheli, V., Solarte, O.: Named Entity Recognition for Spanish language and applications in technology forecasting Reconocimiento de entidades nombradas para el idioma Español y su aplicación en la vigilancia tecnológica. Rev. Antioqueña las Ciencias Comput. y la Ing Softw. 5, 43–47 (2015)

    Google Scholar 

  23. Molina, C.A.C., Gutierrez, R.E., Solarte, O.: Prototipo para el reconocimiento de entidades nombradas en el idioma Español. In: 2015 10th Colombian Computing Conference, 10CCC 2015, pp. 364–371 (2015)

    Google Scholar 

  24. Copara, J., Ochoa, J., Thorne, C., Glavas, G.: Exploring unsupervised features in conditional random fields for spanish named entity recognition. In: Proceedings - 2016 5th Brazilian Conference on Intelligent Systems, BRACIS 2016, pp. 283–288 (2017)

    Google Scholar 

  25. Ziman, K., Heusser, A.C., Fitzpatrick, P.C., et al.: Is automatic speech-to-text transcription ready for use in psychological experiments? Behav. Res. Methods 50, 2597–2605 (2018). https://doi.org/10.3758/s13428-018-1037-4

    Article  Google Scholar 

  26. Yu, D., Deng, L.: Automatic Speech Recognition. Springer, London (2015)

    MATH  Google Scholar 

  27. Symeonidis, S., Effrosynidis, D., Arampatzis, A.: A comparative evaluation of pre-processing techniques and their interactions for Twitter sentiment analysis. Expert Syst. Appl. 110, 298–310 (2018). https://doi.org/10.1016/J.ESWA.2018.06.022

    Article  Google Scholar 

  28. Krouska, A., Troussas, C., Virvou, M.: The effect of preprocessing techniques on Twitter sentiment analysis. In: 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA), pp. 1–5. IEEE (2016)

    Google Scholar 

  29. Moreno, E.I., Dra, A.M., Dra, C., Azc, P.: Reconocimiento y clasificación automatizada de Entidades Nombradas en documentos medievales (s. XIV): Libro Becerro de las Behetrías. Universidad III de Madrid (2017)

    Google Scholar 

  30. Alicia Pérez, M., Carolina Cardoso, A.: Técnicas de extracción de entidades con nombre. Intel. Artif. 17, 3–12 (2014)

    Google Scholar 

  31. Stanford NLP Group. Stanford Named Entity Recognizer (NER) (2019). https://nlp.stanford.edu/software/CRF-NER.html

  32. Korobov, M.: Sklearn Crfsuite (2015). https://sklearn-crfsuite.readthedocs.io/en/latest/tutorial.html. Accessed 3 June 2019

  33. Kudo, T.: CRF++: Yet Another CRF toolkit (2003)

    Google Scholar 

  34. Explosion AI. Industrial-Strength Natural Language Processing. In: Train. named entity recognizer (2015). https://spacy.io/. Accessed 3 June 2019

  35. Al Omran, F.N.A., Treude, C.: Choosing an NLP library for analyzing software documentation: a systematic literature review and a series of experiments. In: IEEE International Working Conference on Mining Software Repositories, pp. 187–197 (2017)

    Google Scholar 

  36. King, B.E., Reinold, K.: Natural language processing Recipes (2019)

    Google Scholar 

  37. Nursalman, M., Kusnendar, J., Fadhila, U.F.: Implementation of k-nearest neighbor with cosine similarity for classification abstract international journal of computer science. In: 2018 International Conference on Information Technology Systems and Innovation, ICITSI 2018 - Proceedings, pp. 43–48. IEEE (2019)

    Google Scholar 

  38. Yang, Z., Yang, D., Dyer, C., et al.: Hierarchical attention networks for document classificatio. In: Proceedings of NAACL-HLT, pp. 1480–1489 (2016)

    Google Scholar 

  39. Kim, D., Seo, D., Cho, S., Kang, P.: Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec. Inf. Sci. (Ny) 477, 15–29 (2019). https://doi.org/10.1016/j.ins.2018.10.006

    Article  Google Scholar 

  40. Hakim, A.A., Erwin, A., Eng, K.I., et al.: Automated document classification for news article in Bahasa Indonesia based on term frequency inverse document frequency (TF-IDF) approach. In: Proceedings - 2014 6th International Conference on Information Technology and Electrical Engineering: Leveraging Research and Technology Through University-Industry Collaboration, ICITEE 2014, pp. 0–3 (2015)

    Google Scholar 

  41. Nakamori, Y.: Performance comparison of TF*IDF, LDA and paragraph vector for document classification. Knowl. Syst. Sci. 2, 225–235 (2016). https://doi.org/10.1201/b15155

    Article  Google Scholar 

Download references

Acknowledgment

This research was supported by the vice-rectorate of investigations of the Universidad del Azuay. We thank our colleagues from Laboratorio de Investigación y Desarrollo en Informática (LIDI) de la Universidad del Azuay who provided insight and expertise that greatly assisted this research.

Author information

Authors and Affiliations

  1. Universidad del Azuay, Av. 24 de Mayo 7-77, Cuenca, Ecuador

    Andrea Trujillo, Marcos Orellana & María Inés Acosta

Authors
  1. Andrea Trujillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcos Orellana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. María Inés Acosta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Trujillo .

Editor information

Editors and Affiliations

  1. ESPE, 171-5-23, Universidad de las Fuerzas Armadas, Sangolqui, Ecuador

    Efrain Fosenca C

  2. Dept de Ciencias de la Compu y Elect, Universidad Técnica Particular, Loja, Ecuador

    Germania Rodríguez Morales

  3. Escuela de Ciencias de la Computación, Universidad del Azuay, Cuenca, Ecuador

    Marcos Orellana Cordero

  4. Mz 55, Villa 4, University of Guayaquil, Guayaquil, Ecuador

    Miguel Botto-Tobar

  5. Universidad del Azuay, Cuenca, Ecuador

    Esteban Crespo Martínez

  6. Universidad del Azuay, Cuenca, Ecuador

    Andrés Patiño León

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Trujillo, A., Orellana, M., Acosta, M.I. (2020). Design of Emergency Call Record Support System Applying Natural Language Processing Techniques. In: Fosenca C, E., Rodríguez Morales, G., Orellana Cordero, M., Botto-Tobar, M., Crespo Martínez, E., Patiño León, A. (eds) Information and Communication Technologies of Ecuador (TIC.EC). TICEC 2019. Advances in Intelligent Systems and Computing, vol 1099. Springer, Cham. https://doi.org/10.1007/978-3-030-35740-5_4

Download citation

Publish with us

Policies and ethics

Search

Navigation