Abbasi, O. R. & Alesheikh, A. A. 2023, A Place Recommendation Approach Using Word Embeddings in Conceptual Spaces, IEEE Access, 11, PP. 11871–11879, https://doi.org/ 10.1109/ACCESS.2023.3241806.
Abdollah Pour, M. M. & Momtazi, S. 2022, Comparative study of text representation and learning for Persian named entity recognition, ETRI Journal, 44(5), PP. 794–804, https://doi.org/ 10.4218/etrij.2021-0269.
Acheson, E., De Sabbata, S. & Purves, R. S. 2017, A quantitative analysis of global gazetteers: Patterns of coverage for common feature types, Computers, Environment and Urban Systems, 64, PP. 309–320, https://doi.org/ 10.1016/j.compenvurbsys.2017.03.007.
Acheson, E. & Purves, R. S. 2021, Extracting and modeling geographic information from scientific articles, PLOS ONE, 16(1), PP. e0244918, https://doi.org/10.1371/journal. pone.0244918.
Adams, B. 2017, Wāhi, a discrete global grid gazetteer built using linked open data, International Journal of Digital Earth, 10(5), PP. 490–503, https://doi.org/10.1080/17538947. 2016.1229819.
Ahmadi, F. & Moradi, H. 2015, A hybrid method for Persian Named Entity Recognition, 2015 7th Conference on Information and Knowledge Technology (IKT), 2015 7th Conference on Information and Knowledge Technology (IKT), PP. 1–7, https://doi.org/ 10.1109/IKT.2015.7288806.
Aldstadt, J. 2010, Spatial Clustering, Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications, Springer, Berlin, Heidelberg, PP. 279–300, https://doi.org/ 10.1007/978-3-642-03647-7_15.
Asgari-Bidhendi, M., Janfada, B., Roshani Talab, O. R. & Minaei-Bidgoli, B. 2021, ParsNER-Social: A Corpus for Named Entity Recognition in Persian Social Media Texts, Journal of AI and Data Mining, 9(2), PP. 181–192, https://doi.org/ 10.22044/jadm.2020. 9949.2143.
Avvenuti, M., Cresci, S., Del Vigna, F., Fagni, T. & Tesconi, M. 2018, CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing, Information Systems Frontiers, 20(5), PP. 993–1011, https://doi.org/10.1007/s10796-018-9833-z.
Ayala-Izurieta, J. E., Márquez, C. O., García, V. J., Recalde-Moreno, C. G., Rodríguez-Llerena, M. V. & Damián-Carrión, D. A. 2017, Land Cover Classification in an Ecuadorian Mountain Geosystem Using a Random Forest Classifier, Spectral Vegetation Indices, and Ancillary Geographic Data, Geosciences, 7(2), PP. 34, https://doi.org/ 10.3390/geosciences7020034.
Bahrehdar, A. R., Adams, B. & Purves, R. S. 2020, Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city, Computers, Environment and Urban Systems, 84, PP. 101524, https://doi.org/ 10.1016/j.compenvurbsys.2020.101524.
Berragan, C., Singleton, A., Calafiore, A. & Morley, J. 2023, Transformer based named entity recognition for place name extraction from unstructured text, International Journal of Geographical Information Science, 37(4), PP. 747–766, https://doi.org/10.1080/ 13658816.2022.2133125.
Biau, G. & Scornet, E. 2016, A random forest guided tour, TEST, 25(2), PP. 197–227, https://doi.org/10.1007/s11749-016-0481-7.
Blanchi, A., Fusco, G., Emsellem, K. & Cadorel, L. 2022, Studying Urban Space from Textual Data: Toward a Methodological Protocol to Extract Geographic Knowledge from Real Estate Ads, Computational Science and Its Applications – ICCSA 2022 Workshops: Malaga, Spain, July 4–7, 2022, Proceedings, Part II, PP. 520–537, https://doi.org/10.1007/ 978-3-031-10562-3_37.
Bokaei, M. H., Sepahvand, A. & Nouri, M. 2021, Improving Persian Named Entity Recognition Through Multi Task Learning, International Journal of Information and Communication Technology Research, 13(2), PP. 39–48, https://doi.org/10.52547/itrc.13.2.39.
Breiman, L. 2001, Random Forests, Machine Learning, 45(1), PP. 5–32, https://doi.org/ 10.1023/A:1010933404324.
Brindley, P., Goulding, J. & Wilson, M. L. 2018, Generating vague neighbourhoods through data mining of passive web data, International Journal of Geographical Information Science, 32(3), PP. 498–523, https://doi.org/10.1080/ 13658816.2017.1400549.
Cadorel, L., Blanchi, A. & Tettamanzi, A. G. B. 2021, Geospatial Knowledge in Housing Advertisements: Capturing and Extracting Spatial Information from Text, Proceedings of the 11th Knowledge Capture Conference, PP. 41–48, https://doi.org/10.1145/3460210. 3493547.
Cardoso, S. D., Amanqui, F. K., Serique, K. J. A., dos Santos, J. L. C. & Moreira, D. A. 2016, SWI: A Semantic Web Interactive Gazetteer to support Linked Open Data, Future Generation Computer Systems, 54, PP. 389–398, https://doi.org/10.1016/j.future.2015.05.006.
Chang, S., Wang, Z., Mao, D., Guan, K., Jia, M. & Chen, C. 2020, Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data, Remote Sensing, 12(15), PP. 2488, https://doi.org/10.3390/rs12152488.
Chen, H., Vasardani, M., Winter, S. & Tomko, M. 2018, A Graph Database Model for Knowledge Extracted from Place Descriptions, ISPRS International Journal of Geo-Information, 7(6), PP. 221, https://doi.org/10.3390/ijgi7060221.
Chen, X., Vo, H., Wang, Y. & Wang, F. 2018, A framework for annotating OpenStreetMap objects using geo-tagged tweets, GeoInformatica, 22(3), PP. 589–613, https://doi.org/10.1007/s10707-018-0323-8.
Cheng, C., Zhang, T., Su, K., Gao, P. & Shen, S. 2019, Assessing the Intensity of the Population Affected by a Complex Natural Disaster Using Social Media Data, ISPRS International Journal of Geo-Information, 8(8), PP. 358, https://doi.org/10.3390/ ijgi8080358.
Chopin, J. & Caneppele, S. 2019, Geocoding child sexual abuse: An explorative analysis on journey to crime and to victimization from French police data, Child Abuse & Neglect, 91, PP. 116–130, https://doi.org/10.1016/ j.chiabu.2019.03.001.
Clementini, E., Di Felice, P. & van Oosterom, P. 1993, A small set of formal topological relationships suitable for end-user interaction, Advances in Spatial Databases, PP. 277–295, https://doi.org/10.1007/3-540-56869-7_16.
Cruz, P., Vanneschi, L., Painho, M. & Rita, P. 2022, Automatic Identification of Addresses: A Systematic Literature Review, ISPRS International Journal of Geo-Information, 11(1), PP. 11, https://doi.org/10.3390/ijgi11010011.
de Bruijn, J. A., de Moel, H., Jongman, B., de Ruiter, M. C., Wagemaker, J. & Aerts, J. C. J. H. 2019, A global database of historic and real-time flood events based on social media, Scientific Data, 6(1), PP. 311, https://doi.org/ 10.1038/s41597-019-0326-9.
De Longueville, B., Ostländer, N. & Keskitalo, C. 2010, Addressing vagueness in Volunteered Geographic Information (VGI)–A case study, International Journal of Spatial Data Infrastructures Research, 5, PP. 1725–0463.
Deidda, M., Pala, A. & Vacca, G. 2013, An example of a tourist location-based service (LBS) with open-source software, Applied Geomatics, 5(1), PP. 73–86, https://doi.org/ 10.1007/s12518-012-0097-x.
Delmelle, E. C. & Nilsson, I. 2021, The language of neighborhoods: A predictive-analytical framework based on property advertisement text and mortgage lending data, Computers, Environment and Urban Systems, 88, PP. 101658, https://doi.org/10.1016/ j.compenvurbsys.2021.101658.
Derungs, C. & Purves, R. S. 2016, Mining nearness relations from an n-grams Web corpus in geographical space, Spatial Cognition & Computation, 16(4), PP. 301–322, https://doi.org/10.1080/13875868. 2016. 1246553.
Dewandaru, A., Widyantoro, D. H. & Akbar, S. 2020, Event Geoparser with Pseudo-Location Entity Identification and Numerical Argument Extraction Implementation and Evaluation in Indonesian News Domain, ISPRS International Journal of Geo-Information, 9(12), PP. 712, https://doi.org/10.3390/ ijgi9120712.
Etemad, S., Mosayebi, R., Alexani Khodavirdian, T., Dastan, E., Salari Telmadarreh, A., Jafari, M. & Rafiei, S. 2023, Clustering of Urban Traffic Patterns by K-Means and Dynamic Time Warping: Case Study, https://doi.org/ 10.48550/arXiv.2309.09830.
Farahani, M., Gharachorloo, M., Farahani, M. & Manthouri, M. 2021, ParsBERT: Transformer-based Model for Persian Language Understanding, Neural Processing Letters, 53(6), PP. 3831–3847, https://doi.org/ 10.1007/ s11063-021-10528-4.
Gao, S., Janowicz, K., Montello, D. R., Hu, Y., Yang, J.-A., McKenzie, G., Ju, Y., Gong, L., Adams, B. & Yan, B. 2017, A data-synthesis-driven method for detecting and extracting vague cognitive regions, International Journal of Geographical Information Science, 31(6), PP. 1245–1271, https://doi.org/10.1080/ 13658816.2016.1273357.
Gao, S., Li, L., Li, W., Janowicz, K. & Zhang, Y. 2017, Constructing gazetteers from volunteered Big Geo-Data based on Hadoop, Computers, Environment and Urban Systems, 61, PP. 172–186, https://doi.org/10.1016/ j.compenvurbsys.2014.02.004.
Gao, S., Li, L., Li, W., Janowicz, K. & Zhang, Y. 2017, Constructing gazetteers from volunteered Big Geo-Data based on Hadoop, Computers, Environment and Urban Systems, 61, PP. 172–186, https://doi.org/10.1016/ j.compenvurbsys.2014.02.004.
Ghayoomi, M. & Momtazi, S. 2009, Challenges in Developing Persian Corpora from Online Resources, 2009 International Conference on Asian Language Processing, 2009 International Conference on Asian Language Processing, PP. 108–113, https://doi.org/ 10.1109/IALP.2009.31.
Goodchild, M. F. & Hill, L. L. 2008, Introduction to digital gazetteer research, International Journal of Geographical Information Science, 22(10), PP. 1039–1044, https://doi.org/ 10.1080/13658810701850497.
Grace, R. 2021, Toponym usage in social media in emergencies, International Journal of Disaster Risk Reduction, 52, PP. 101923, https://doi.org/10.1016/j.ijdrr.2020.101923.
Gritta, M. 2019, Where are you talking about? Advances and Challenges of Geographic Analysis of Text with Application to Disease Monitoring, https://doi.org/10.17863/ CAM.41821.
Gritta, M., Pilehvar, M. T., Limsopatham, N. & Collier, N. 2018, What’s missing in geographical parsing?, Language Resources and Evaluation, 52(2), PP. 603–623, https://doi.org/10.1007/s10579-017-9385-8.
Grossner, K., Janowicz, K. & Kessler, C. 2016, Place, Period, and Setting for Linked Data Gazetteers, Placing Names, Indiana University Press, Bloomington, IN, PP. 80–96.
Haberman, C. P., Hatten, D., Carter, J. G. & Piza, E. L. 2021, The sensitivity of repeat and near repeat analysis to geocoding algorithms, Journal of Criminal Justice, 73, PP. 101721, https://doi.org/10.1016/j.jcrimjus.2020.101721.
Habib, M. K. 2021, The Challenges of Persian User-generated Textual Content: A Machine Learning-Based Approach, https://doi.org/ 10.48550/arXiv.2101.08087.
Haris, E. & Gan, K. H. 2017, Mining graphs from travel blogs: a review in the context of tour planning, Information Technology & Tourism, 17(4), PP. 429–453, https://doi.org/ 10.1007/s40558-017-0095-2.
Hastings, J. & Hill, L. 2002, Treatment of duplicates in the alexandria digital library gazetteer, Proceedings of GeoScience.
Hill, L. L. 2000, Core Elements of Digital Gazetteers: Placenames, Categories, and Footprints, Research and Advanced Technology for Digital Libraries, PP. 280–290, https://doi.org/10.1007/3-540-45268-0_26.
Hu, X., Al-Olimat, H. S., Kersten, J., Wiegmann, M., Klan, F., Sun, Y. & Fan, H. 2022, GazPNE: annotation-free deep learning for place name extraction from microblogs leveraging gazetteer and synthetic data by rules, International Journal of Geographical Information Science, 36(2), PP. 310–337, https://doi.org/10.1080/13658816.2021.1947507.
Hu, Y. & Adams, B. 2021, Harvesting Big Geospatial Data from Natural Language Texts, Handbook of Big Geospatial Data, Springer International Publishing, Cham, PP. 487–507, https://doi.org/10.1007/978-3-030-55462-0_19.
Hu, Y., Gao, S., Janowicz, K., Yu, B., Li, W. & Prasad, S. 2015, Extracting and understanding urban areas of interest using geotagged photos, Computers, Environment and Urban Systems, 54, PP. 240–254, https://doi.org/ 10.1016/j.compenvurbsys.2015.09.001.
Hu, Y., Gao, S., Janowicz, K., Yu, B., Li, W. & Prasad, S. 2015, Extracting and understanding urban areas of interest using geotagged photos, Computers, Environment and Urban Systems, 54, PP. 240–254, https://doi.org/ 10.1016/j.compenvurbsys.2015.09.001.
Hu, Y., Mao, H. & McKenzie, G. 2019, A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements, International Journal of Geographical Information Science, 33(4), PP. 714–738, https://doi.org/10.1080/13658816.2018.1458986.
Hu, Y. & Wang, J. 2020, How Do People Describe Locations During a Natural Disaster: An Analysis of Tweets from Hurricane Harvey, 11th International Conference on Geographic Information Science (GIScience 2021) - Part I, PP. 6:1-6:16, https://doi.org/ 10.4230/LIPIcs.GIScience.2021.I.6.
Huck, J. J., Whyatt, J. D. & Coulton, P. 2014, Spraycan: A PPGIS for capturing imprecise notions of place, Applied Geography, 55, PP. 229–237, https://doi.org/10.1016/j.apgeog. 2014.09.007.
Idakwo, P. O., Adekanmbi, O., Soronnadi, A. & David, A. 2025, Geo-parsing and analysis of road traffic crash incidents for data-driven emergency response planning, Heliyon, 11(4), https://doi.org/10.1016/j.heliyon.2024.e41067.
Imani, M. B., Chandra, S., Ma, S., Khan, L. & Thuraisingham, B. 2017, Focus location extraction from political news reports with bias correction, 2017 IEEE International Conference on Big Data (Big Data), 2017 IEEE International Conference on Big Data (Big Data), PP. 1956–1964, https://doi.org/ 10.1109/BigData.2017.8258141.
Janowicz, K. & Keßler, C. 2008, The role of ontology in improving gazetteer interaction, International Journal of Geographical Information Science, 22(10), PP. 1129–1157, https://doi.org/10.1080/13658810701851461.
Javidaneh, A., Karimipour, F. & Alinaghi, N. 2020, How Much Do We Learn from Addresses? On the Syntax, Semantics and Pragmatics of Addressing Systems, ISPRS International Journal of Geo-Information, 9(5), PP. 317, https://doi.org/10.3390/ijgi9050317.
Jones, C. B., Purves, R. S., Clough, P. D. & Joho, H. 2008, Modelling vague places with knowledge from the Web, International Journal of Geographical Information Science, 22(10), PP. 1045–1065, https://doi.org/ 10.1080/13658810701850547.
Karimzadeh, M., Pezanowski, S., MacEachren, A. M. & Wallgrün, J. O. 2019, GeoTxt: A scalable geoparsing system for unstructured text geolocation, Transactions in GIS, 23(1), PP. 118–136, https://doi.org/10.1111/tgis.12510.
Keßler, C., Janowicz, K. & Bishr, M. 2009, An agenda for the next generation gazetteer: geographic information contribution and retrieval, Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, PP. 91–100, https://doi.org/ 10.1145/1653771.1653787.
Keßler, C., Maué, P., Heuer, J. T. & Bartoschek, T. 2009, Bottom-Up Gazetteers: Learning from the Implicit Semantics of Geotags, GeoSpatial Semantics, Springer Berlin Heidelberg, Berlin, Heidelberg, PP. 83–102, https://doi.org/10.1007/978-3-642-10436-7_6.
Kim, J., Vasardani, M. & Winter, S. 2015, Harvesting large corpora for generating place graphs, International Workshop on Cognitive Engineering for Spatial Information Processes.
Kordjamshidi, P., Van Otterlo, M. & Moens, M.-F. 2011, Spatial role labeling: Towards extraction of spatial relations from natural language, ACM Trans. Speech Lang. Process., 8(3), https://doi.org/10.1145/2050104.2050105.
Laurini, R. 2015, Geographic Ontologies, Gazetteers and Multilingualism, Future Internet, 7(1), PP. 1–23, https://doi.org/ 10.3390/fi7010001.
Leppämäki, T., Toivonen, T. & Hiippala, T. 2024, Geographical and linguistic perspectives on developing geoparsers with generic resources, International Journal of Geographical Information Science, 38(10), PP. 2039–2060, https://doi.org/10.1080/13658816.2024.2369539.
Levy, D. & Lee, C. K. C. 2011, Neighbourhood identities and household location choice: estate agents’ perspectives, Journal of Place Management and Development, 4(3), PP. 243–263, https://doi.org/10.1108/175383311 11176066.
Li, D., Cova, T. J. & Dennison, P. E. 2017, Using reverse geocoding to identify prominent wildfire evacuation trigger points, Applied Geography, 87, PP. 14–27, https://doi.org/ 10.1016/j.apgeog.2017.05.008.
Li, J., Liu, R. & Xiong, R. 2017, A Chinese Geographic Knowledge Base for GIR, 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), PP. 361–368, https://doi.org/10.1109/CSE-EUC.2017.72.
Li, L., Wang, W., He, B. & Zhang, Y. 2018, A hybrid method for Chinese address segmentation, International Journal of Geographical Information Science, 32(1), PP. 30–48, https://doi.org/10.1080/13658816. 2017.1379084.
Lim, J., Nitta, N., Nakamura, K. & Babaguchi, N. 2019, Constructing Geographic Dictionary from Streaming Geotagged Tweets, ISPRS International Journal of Geo-Information, 8(5), PP. 216, https://doi.org/10.3390/ ijgi8050216.
Liu, Y., Li, R., Chen, K., Yuan, Y., Huang, L. & Yu, H. 2009, KIDGS: A geographical knowledge-informed digital gazetteer service, 2009 17th International Conference on Geoinformatics, 2009 17th International Conference on Geoinformatics, PP. 1–6, https://doi.org/10.1109/GEOINFORMATICS.2009.5293495.
Lynch, K. 1960, The Image of the City, MIT Press.
Machado, I. M. R., de Alencar, R. O., Campos, R. de O. & Davis, C. A. 2011, An ontological gazetteer and its application for place name disambiguation in text, Journal of the Brazilian Computer Society, 17(4), PP. 267–279, https://doi.org/10.1007/s13173-011-0044-4.
Madhulatha, T. S. 2012, AN OVERVIEW ON CLUSTERING METHODS, IOSR Journal of Engineering, 02(04), PP. 719–725, https://doi.org/ 10.9790/3021-0204719725.
Mai, G., Janowicz, K., Prasad, S., Shi, M., Cai, L., Zhu, R., Regalia, B. & Lao, N. 2020, Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online, AGILE: GIScience Series, 1, PP. 1–17, https://doi.org/10.5194/agile-giss-1-13-2020.
Manguinhas, H., Martins, B., Borbinha, J. & Siabato, W. 2009, The DIGMAP geo-temporal Web gazetteer service, e-Perimetron, 4(1), PP. 9–24.
Martins, B. 2011, A Supervised Machine Learning Approach for Duplicate Detection over Gazetteer Records, GeoSpatial Semantics, PP. 34–51, https://doi.org/10.1007/ 978-3-642-20630-6_3.
McKenzie, G. & Janowicz, K. 2015, Where is also about time: A location-distortion model to improve reverse geocoding using behavior-driven temporal semantic signatures, Computers, Environment and Urban Systems, 54, PP. 1–13, https://doi.org/10.1016/ j.compenvurbsys.2015.05.003.
McKenzie, G., Liu, Z., Hu, Y. & Lee, M. 2018, Identifying Urban Neighborhood Names through User-Contributed Online Property Listings, ISPRS International Journal of Geo-Information, 7(10), PP. 388, https://doi.org/ 10.3390/ijgi7100388.
Mehta, A., Kim, D., Allo, N., Odusola, A. O., Malolan, C. & Nwariaku, F. E. 2023, Using parallel geocoding to analyse the spatial characteristics of road traffic injury occurrences across Lagos, Nigeria, BMJ Global Health, 8(5), https://doi.org/ 10.1136/bmjgh-2023-012315.
Merschdorf, H. & Blaschke, T. 2018, Revisiting the Role of Place in Geographic Information Science, ISPRS International Journal of Geo-Information, 7(9), PP. 364, https://doi.org/ 10.3390/ijgi7090364.
Middleton, S. E., Kordopatis-Zilos, G., Papadopoulos, S. & Kompatsiaris, Y. 2018, Location Extraction from Social Media: Geoparsing, Location Disambiguation, and Geotagging, ACM Transactions on Information Systems (TOIS), 36(4), PP. 40:1-40:27, https://doi.org/10.1145/3202662.
Middleton, S. E. & Krivcovs, V. 2016, Geoparsing and Geosemantics for Social Media: Spatiotemporal Grounding of Content Propagating Rumors to Support Trust and Veracity Analysis during Breaking News, ACM Transactions on Information Systems (TOIS), 34(3), PP. 16:1-16:26, https://doi.org/ 10.1145/2842604.
Mohseni, M. & Tebbifakhr, A. 2019, MorphoBERT: a Persian NER System with BERT and Morphological Analysis, Proceedings of the First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers, NSURL 2019, PP. 23–30.
Mortazavi, P. S. & Shamsfard, M. 2009, Named entity recognition in persian texts, 15th National CSI Computer Conference.
Moura, T. H. V. M., Davis Jr., C. A. & Fonseca, F. T. 2017, Reference data enhancement for geographic information retrieval using linked data, Transactions in GIS, 21(4), PP. 683–700, https://doi.org/10.1111/tgis.12238.
Oliveira, M. G. de, Campelo, C. E. C., Baptista, C. de S. & Bertolotto, M. 2016, Gazetteer enrichment for addressing urban areas: a case study, Journal of Location Based Services, 10(2), PP. 142–159, https://doi.org/10.1080/ 17489725.2016.1196755.
Oto-Peralías, D. 2018, What do street names tell us? The ‘city-text’ as socio-cultural data, Journal of Economic Geography, 18(1), PP. 187–211, https://doi.org/10.1093/jeg/lbx030.
Poostchi, H., Zare Borzeshi, E., Abdous, M. & Piccardi, M. 2016, PersoNER: Persian Named-Entity Recognition, Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, COLING 2016, PP. 3381–3389.
Poostchi, H., Zare Borzeshi, E. & Piccardi, M. 2018, BiLSTM-CRF for Persian Named-Entity Recognition ArmanPersoNERCorpus: the First Entity-Annotated Persian Dataset, Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), LREC 2018.
Popescu, A., Grefenstette, G. & Moëllic, P. A. 2008, Gazetiki: automatic creation of a geographical gazetteer, Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries, PP. 85–93, https://doi.org/10.1145/ 1378889.1378906.
Pustejovsky, J. 2017, ISO-Space: Annotating Static and Dynamic Spatial Information, Handbook of Linguistic Annotation, Springer Netherlands, Dordrecht, PP. 989–1024, https://doi.org/10.1007/978-94-024-0881-2_37.
Qazi, U., Imran, M. & Ofli, F. 2020, GeoCoV19: a dataset of hundreds of millions of multilingual COVID-19 tweets with location information, SIGSPATIAL Special, 12(1), PP. 6–15, https://doi.org/10.1145/3404820.3404823.
Rattenbury, T., Good, N. & Naaman, M. 2007, Towards automatic extraction of event and place semantics from flickr tags, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, PP. 103–110, https://doi.org/10.1145/1277741.1277762.
Ringland, N., Dai, X., Hachey, B., Karimi, S., Paris, C. & Curran, J. R. 2019, NNE: A Dataset for Nested Named Entity Recognition in English Newswire, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL 2019, PP. 5176–5181, https://doi.org/10.18653/v1/P19-1510.
Sabzali Yameqani, A. & Alesheikh, A. A. 2019, Evaluating a location distortion model to improve reverse geocoding through temporal semantic signatures, Computers, Environment and Urban Systems, 77, PP. 101349, https://doi.org/10.1016/j.compenvurbsys.2019.101349.
Sabzali Yameqani, A. & Alesheikh, A. A. 2025, Enhancing Reverse Geocoding With Weather Data: Modeling Human Check-In Behavior in California and New York for Smart Cities, Transactions in GIS, 29(3), PP. e70059, https://doi.org/10.1111/tgis.70059.
Samarin, M. & Sharma, M. 2020, A Neighborhood-level Perspective of Real Estate Determinants in Three U.S. Cities, International Journal of Geospatial and Environmental Research, 7(3).
Schiersch, M., Mironova, V., Schmitt, M., Thomas, P., Gabryszak, A. & Hennig, L. 2018, A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events, Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), LREC 2018.
Schubert, E., Sander, J., Ester, M., Kriegel, H. P. & Xu, X. 2017, DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN, ACM Transactions on Database Systems (TODS), 42(3), PP. 19:1-19:21, https://doi.org/10.1145/3068335.
Shahshahani, M. S., Mohseni, M., Shakery, A. & Faili, H. 2019, PAYMA: A Tagged Corpus of Persian Named Entities, Signal and Data Processing, 16(1), PP. 91–110, https://doi.org/10.29252/jsdp.16.1.91.
Shamsfard, M. 2011, Challenges and open problems in Persian text processing, Proceedings of LTC, PP. 65–69.
Stock, K., Jones, C. B., Russell, S., Radke, M., Das, P. & Aflaki, N. 2022, Detecting geospatial location descriptions in natural language text, International Journal of Geographical Information Science, 36(3), PP. 547–584, https://doi.org/10.1080/13658816. 2021.1987441.
Stock, K., Jones, C. B., Russell, S., Radke, M., Das, P. & Aflaki, N. 2022, Detecting geospatial location descriptions in natural language text, International Journal of Geographical Information Science, 36(3), PP. 547–584, https://doi.org/10.1080/13658816. 2021.1987441.
Stock, K. & Yousaf, J. 2018, Context-aware automated interpretation of elaborate natural language descriptions of location through learning from empirical data, International Journal of Geographical Information Science, 32(6), PP. 1087–1116, https://doi.org/ 10.1080/13658816.2018.1432861.
Suat-Rojas, N., Gutierrez-Osorio, C. & Pedraza, C. 2022, Extraction and Analysis of Social Networks Data to Detect Traffic Accidents, Information, 13(1), PP. 26, https://doi.org/ 10.3390/info13010026.
Surano, F. V., Porfiri, M. & Rizzo, A. 2022, Analysis of lockdown perception in the United States during the COVID-19 pandemic, The European Physical Journal Special Topics, 231(9), PP. 1625–1633, https://doi.org/ 10.1140/epjs/s11734-021-00265-z.
Suwaileh, R., Elsayed, T., Imran, M. & Sajjad, H. 2022, When a disaster happens, we are ready: Location mention recognition from crisis tweets, International Journal of Disaster Risk Reduction, 78, PP. 103107, https://doi.org/ 10.1016/j.ijdrr.2022.103107.
Taher, E., Hoseini, S. A. & Shamsfard, M. 2019, Beheshti-NER: Persian named entity recognition Using BERT, Proceedings of the First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019 - Short Papers, NSURL 2019, PP. 37–42.
Talen, E. & Jeong, H. 2019, What is the value of ‘main street’? Framing and testing the arguments, Cities, 92, PP. 208–218, https://doi.org/10.1016/j.cities.2019.03.023.
Tewari, S. & Beynon, D. 2018, Changing neighbourhood character in Melbourne: point Cook a case study, Journal of Urban Design, 23(3), PP. 456–464, https://doi.org/10.1080/ 13574809.2017.1383152.
Tian, Q., Ren, F., Hu, T., Liu, J., Li, R. & Du, Q. 2016, Using an Optimized Chinese Address Matching Method to Develop a Geocoding Service: A Case Study of Shenzhen, China, ISPRS International Journal of Geo-Information, 5(5), PP. 65, https://doi.org/ 10.3390/ijgi5050065.
Twaroch, F. A. & Jones, C. B. 2010, A web platform for the evaluation of vernacular place names in automatically constructed gazetteers, Proceedings of the 6th Workshop on Geographic Information Retrieval, PP. 1–2, https://doi.org/10.1145/1722080.1722098.
Wallgrün, J. O., Klippel, A. & Baldwin, T. 2014, Building a corpus of spatial relational expressions extracted from web documents, Proceedings of the 8th Workshop on Geographic Information Retrieval, PP. 1–8, https://doi.org/10.1145/2675354.2675702.
Wang, W. & Stewart, K. 2015, Spatiotemporal and semantic information extraction from Web news reports about natural hazards, Computers, Environment and Urban Systems, 50, PP. 30–40, https://doi.org/10.1016/ j.compenvurbsys.2014.11.001.
Won, M., Murrieta-Flores, P. & Martins, B. 2018, Ensemble Named Entity Recognition (NER): Evaluating NER Tools in the Identification of Place Names in Historical Corpora, Frontiers in Digital Humanities, 5, https://doi.org/ 10.3389/fdigh.2018.00002.
Yang, J., Yu, M., Qin, H., Lu, M. & Yang, C. 2019, A Twitter Data Credibility Framework—Hurricane Harvey as a Use Case, ISPRS International Journal of Geo-Information, 8(3), PP. 111, https://doi.org/ 10.3390/ijgi8030111.
Zhang, C., He, B., Guo, R. & Ma, D. 2023, A graph-based approach for representing addresses in geocoding, Computers, Environment and Urban Systems, 100, PP. 101937, https://doi.org/10.1016/ j.compenvurbsys.2022.101937.
Zhang, T., Shen, S., Cheng, C., Su, K. & Zhang, X. 2021, A topic model based framework for identifying the distribution of demand for relief supplies using social media data, International Journal of Geographical Information Science, 35(11), PP. 2216–2237, https://doi.org/10.1080/13658816.2020.1869746.
Zhang, W. & Gelernter, J. 2014, Geocoding location expressions in Twitter messages: A preference learning method, Journal of Spatial Information Science, (9), PP. 37–70.
Zhang, Y., Ma, Q., Chiang, Y.-Y., Knoblock, C., Zhang, X., Yang, P., Gao, M. & Hu, X. 2019, Extracting geographic features from the Internet: A geographic information mining framework, Knowledge-Based Systems, 174, PP. 57–72, https://doi.org/10.1016/j.knosys. 2019.02.031.
Zhang, Y., Wu, W., Wang, Q. & Su, F. 2017, A Geo-Event-Based Geospatial Information Service: A Case Study of Typhoon Hazard, Sustainability, 9(4), PP. 534, https://doi.org/ 10.3390/su9040534.
Windfuhr, G. 2013, The Iranian Languages, Routledge, London, https://doi.org/10.4324/ 9780203641736.