Using natural language processing technologies to automate the coordination of objects from travelogs

Cartography

UDC:

528.9:004.6

DOI:

10.22389/0016-7126-2025-1019-5-9-15

1 Kolesnikov A.A.

2 Zhdanov S.S.

Year:

2025

№:

1019

Pages:

9-15

Novosibirsk State Technical University (NSTU), language center "Lingua"

1,

2,

Abstract:

The authors discuss the concept of travelogue and its connection with spatial data, the importance of using this type of artistic work, including creation of cartographic materials. In addition to manual processing of travelogue texts, algorithms and natural language methods can be used for automation. Existing examples of manipulations containing geographical names and integrating the results into databases of GIS are considered. The degree of automation using natural language processing (NLP) technologies and the accuracy of creating a route database for a random set of documents is also analyzed. Expanding the parameters of the Movement Action Events object and a quantitative assessment of their relevance among the elements with similar values are proposed. The analyzed set of documents contains 150 texts written between the 18th and 20th centuries, with a total volume of 46 thousand words (with average number of 308 per document including addresses). The results of comparing several pre-trained neural networks variants (large language models as well) are presented in terms of the final accuracy of determining travel routes using the given texts, including those additionally trained using an additional data set

Keywords:

geoinformation technologies, large language models, machine learning, natural language processing, route, text, travelogue

The paper is prepared within the framework of the Russian Science Foundation grant No. 24-28-01431

References:

1. Karpik A.P., Lisitsky D.V., Musikhin I.A. (2023) Development of geospatial activities in Russia: strategic directions and priorities. Geodezia i Kartografia, 84(12), pp. 49-58. (In Russian). DOI: 10.22389/0016-7126-2023-1002-12-49-58.

2. Kublitskaya O. V. Sub"ektnaya struktura puteshestvii «massovogo sentimentalizma» (na materiale «Puteshestviya v Malorossiyu» P. I. Shalikova). Nauchnyi dialog, 2022, Vol. 11, no. 7, pp. 284–303. DOI: 10.24224/2227-1295-2022-11-7-284-303.

3. Kulikova A. A. Putevaya proza v russkoi literature kontsa XVIII nachala XIX veka. Uchenye zapiski Rossiiskogo gosudarstvennogo sotsial'nogo universiteta, 2008, no. 4 (60), pp. 241–243.

4. Maiga A. A. Literaturnyi travelog: spetsifika zhanra. Filologiya i kul'tura, 2014, no. 3 (37), pp. 254–259.

5. Russkii travelog XVIII–XX vekov: marshruty, toposy, zhanry i narrativy. Pod red. T. I. Pecherskoi, N. V. Konstantinovoi. Novosibirsk: Izd-vo NGPU, 2016, 462 p.

6. Stepanov V. K., Madzhumder M. Sh., Begunova D. D. Metodika primeneniya bol'shoi yazykovoi modeli ChatGPT v bibliotechno-bibliograficheskoi deyatel'nosti. Nauchnye i tekhnicheskie biblioteki, 2024, no. 4, pp. 86–108. DOI: 10.33186/1027-3689-2024-4-86-108.

7. Bourgonje P., Schneider J.M., Rehm G., Sasaki F. (2016) Processing document collections to automatically extract linked data: semantic storytelling technologies for smart curation workflows. Proceedings of the 2nd International Workshop on Natural Language Generation and the Semantic Web (WebNLG). ACL, Edinburgh, UK, pp. 13–16.

8. Gervas P. (2014) Metrics for desired structural features for narrative renderings of game logs. Entertainment Computing, no. 5 (4), pp. 245–250. DOI: 10.1016/j.entcom.2014.08.001.

9. Moreno-Schneider J., Bourgonje P., Rehm G. (2017) Towards User Interfaces for Semantic Storytelling. In: Yamamoto S. (eds) Human Interface and the Management of Information: Supporting Learning, Decision-Making and Collaboration. HIMI 2017. Lecture Notes in Computer Science, 10274. Springer, Cham. pp. 403–421. DOI: 10.1007/978-3-319-58524-6_32.

10. Nothman J., Ringland N., Radford W., Murphy T., Curran J. R. (2012) Learning multilingual named entity recognition from Wikipedia. Artificial Intelligence, no. 194, pp. 151–175. DOI: 10.1016/J.ARTINT.2012.03.006.

11. Poulakos S., Kapadia M., Schupfer A., Zund F., Sumner R., Gross M. (2015) Towards an accessible interface for story world building. AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. pp. 42–48.

12. Rehm G., Moreno-Schneider J. ћ., Bourgonje P., Srivastava A., Nehring J., Berger A., Kоnig L., Rаuchle S., Gerth J. (2017) Event detection and semantic storytelling: generating a travelogue from a large collection of personal letters. Events and Stories in the News Workshop. Association for Computational Linguistics, Vancouver, Canada, pp. 42–51. DOI: 10.18653/v1/W17-2707.

13. Turner S. R. (2014) The Creative Process: A Computer Model of Storytelling and Creativity. Taylor and Francis, 308 p.

14. Wood M. D. (2008) Exploiting semantics for personalized story creation. 2008 IEEE International Conference on Semantic Computing. IEEE Computer Society (ICSC Т08). Washington, DC. pp. 402–409. DOI: 10.1109/ICSC.2008.10.

15. Yang B., Mitchell T. (2016) Joint extraction of events and entities within a document context. 2016 Conf. of the North American Chapter of the ACL: Human Language Technologies. ACL. pp. 289–299. DOI: 10.48550/arXiv.1609.03632.

Citation:

Kolesnikov A.A.,

Zhdanov S.S.,

(2025) Using natural language processing technologies to automate the coordination of objects from travelogs. Geodesy and cartography = Geodeziya i Kartografiya, 86(5), pp. 9-15. (In Russian). DOI: 10.22389/0016-7126-2025-1019-5-9-15