{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T11:45:46Z","timestamp":1747309546233,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":36,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819608461"},{"type":"electronic","value":"9789819608478"}],"license":[{"start":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T00:00:00Z","timestamp":1734134400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T00:00:00Z","timestamp":1734134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-0847-8_14","type":"book-chapter","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T04:27:22Z","timestamp":1734064042000},"page":"198-212","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["GeoGLUE: A Chinese GeoGraphic Language Understanding Evaluation Benchmark"],"prefix":"10.1007","author":[{"given":"Dongyang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruixue","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boli","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengjun","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,14]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Antenucci, J.C., Brown, K., Croswell, P.L., Kevany, M.J., Archer, H.: Geographic Information Systems: a guide to the technology, vol.\u00a0115 (1991)","DOI":"10.1007\/978-1-4684-6533-4"},{"key":"14_CR2","doi-asserted-by":"publisher","unstructured":"Aydin, B., Gensel, J., Genoud, P., Calabretto, S., Tellez, B.: An architecture for surroundings discovery by linking 3d models and LOD cloud. In: MobiGIS, pp. 9\u201316 (2013). https:\/\/doi.org\/10.1145\/2534190.2534198","DOI":"10.1145\/2534190.2534198"},{"key":"14_CR3","doi-asserted-by":"publisher","unstructured":"Barbieri, F., Camacho-Collados, J., Anke, L.E., Neves, L.: Tweeteval: unified benchmark and comparative evaluation for tweet classification. In: Findings of EMNLP, pp. 1644\u20131650 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.148","DOI":"10.18653\/v1\/2020.findings-emnlp.148"},{"key":"14_CR4","doi-asserted-by":"publisher","unstructured":"Chalkidis, I., et al.: Lexglue: a benchmark dataset for legal language understanding in English. In: ACL, pp. 4310\u20134330 (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.297","DOI":"10.18653\/v1\/2022.acl-long.297"},{"issue":"2","key":"14_CR5","first-page":"35","volume":"19","author":"PJ Daugherty","year":"1998","unstructured":"Daugherty, P.J., Stank, T.P., Ellinger, A.E.: Leveraging logistics\/distribution capabilities: the effect of logistics service on market share. J. Bus. Logist. 19(2), 35 (1998)","journal-title":"J. Bus. Logist."},{"key":"14_CR6","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: ACL, pp. 4171\u20134186 (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"14_CR7","doi-asserted-by":"publisher","unstructured":"DeYoung, J., et al.: ERASER: a benchmark to evaluate rationalized NLP models. In: ACL, pp. 4443\u20134458 (2020), https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.408","DOI":"10.18653\/v1\/2020.acl-main.408"},{"key":"14_CR8","doi-asserted-by":"publisher","unstructured":"Ding, R., Chen, B., Xie, P., Huang, F., Li, X., Zhang, Q., Xu, Y.: A multi-modal geographic pre-training method. CoRR abs\/2301.04283 (2023). https:\/\/doi.org\/10.48550\/arXiv.2301.04283","DOI":"10.48550\/arXiv.2301.04283"},{"key":"14_CR9","doi-asserted-by":"publisher","unstructured":"ElSherief, M., et al.: Latent hatred: a benchmark for understanding implicit hate speech. In: EMNLP, pp. 345\u2013363 (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.29","DOI":"10.18653\/v1\/2021.emnlp-main.29"},{"key":"14_CR10","doi-asserted-by":"publisher","unstructured":"Ham, J., Choe, Y.J., Park, K., Choi, I., Soh, H.: Kornli and korsts: New benchmark datasets for Korean natural language understanding. In: EMNLP, pp. 422\u2013430 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.39","DOI":"10.18653\/v1\/2020.findings-emnlp.39"},{"key":"14_CR11","unstructured":"Hu, J., Ruder, S., Siddhant, A., Neubig, G., Firat, O., Johnson, M.: XTREME: a massively multilingual multi-task benchmark for evaluating cross-lingual generalization, pp. 4411\u20134421 (2020). https:\/\/arxiv.org\/abs\/2003.11080"},{"key":"14_CR12","doi-asserted-by":"publisher","unstructured":"Huang, Z., et al.: Geosqa: a benchmark for scenario-based question answering in the geography domain at high school level. In: EMNLP, pp. 5865\u20135870 (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1597","DOI":"10.18653\/v1\/D19-1597"},{"key":"14_CR13","doi-asserted-by":"publisher","unstructured":"Koto, F., Rahimi, A., Lau, J.H., Baldwin, T.: Indolem and indobert: a benchmark dataset and pre-trained language model for Indonesian NLP. In: COLING, pp. 757\u2013770 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.66","DOI":"10.18653\/v1\/2020.coling-main.66"},{"key":"14_CR14","doi-asserted-by":"publisher","unstructured":"Kwiatkowski, T., et al.: Natural questions: a benchmark for question answering research. TACL 7, 452\u2013466 (2019). https:\/\/doi.org\/10.1162\/tacl_a_00276","DOI":"10.1162\/tacl_a_00276"},{"key":"14_CR15","doi-asserted-by":"publisher","unstructured":"Li, H., Lu, W., Xie, P., Li, L.: Neural Chinese address parsing. In: NAACL, pp. 3421\u20133431 (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1346","DOI":"10.18653\/v1\/n19-1346"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Li, Z., Kim, J., Chiang, Y., Chen, M.: Spabert: a pretrained language model from geographic data for geo-entity representation. In: Findings of EMNLP, pp. 2757\u20132769 (2022). https:\/\/aclanthology.org\/2022.findings-emnlp.200","DOI":"10.18653\/v1\/2022.findings-emnlp.200"},{"key":"14_CR17","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach abs\/1907.11692 (2019). http:\/\/arxiv.org\/abs\/1907.11692"},{"key":"14_CR18","unstructured":"Mandl, T., et al.: An evaluation resource for geographic information retrieval. In: LREC (2008). http:\/\/www.lrec-conf.org\/proceedings\/lrec2008\/summaries\/8.html"},{"key":"14_CR19","doi-asserted-by":"publisher","unstructured":"Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., Kiela, D.: Adversarial NLI: a new benchmark for natural language understanding. In: ACL, pp. 4885\u20134901 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.441","DOI":"10.18653\/v1\/2020.acl-main.441"},{"key":"14_CR20","doi-asserted-by":"publisher","unstructured":"Parcalabescu, L., Cafagna, M., Muradjan, L., Frank, A., Calixto, I., Gatt, A.: VALSE: a task-independent benchmark for vision and language models centered on linguistic phenomena. In: ACL, pp. 8253\u20138280. Association for Computational Linguistics (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.567","DOI":"10.18653\/v1\/2022.acl-long.567"},{"key":"14_CR21","doi-asserted-by":"publisher","unstructured":"Pezzelle, S., Greco, C., Gandolfi, G., Gualdoni, E., Bernardi, R.: Be different to be better! A benchmark to leverage the complementarity of language and vision. In: Findings of EMNLP. pp. 2751\u20132767 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.248","DOI":"10.18653\/v1\/2020.findings-emnlp.248"},{"key":"14_CR22","doi-asserted-by":"publisher","unstructured":"Qi, F., Yang, Y., Yi, J., Cheng, Z., Liu, Z., Sun, M.: Quoter: a benchmark of quote recommendation for writing. In: ACL, pp. 336\u2013348 (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.27","DOI":"10.18653\/v1\/2022.acl-long.27"},{"key":"14_CR23","doi-asserted-by":"publisher","unstructured":"Raganato, A., Pasini, T., Camacho-Collados, J., Pilehvar, M.T.: Xl-wic: A multilingual benchmark for evaluating semantic contextualization. In: EMNLP, pp. 7193\u20137206 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.584","DOI":"10.18653\/v1\/2020.emnlp-main.584"},{"key":"14_CR24","doi-asserted-by":"publisher","unstructured":"Ravichander, A., Naik, A., Ros\u00e9, C.P., Hovy, E.H.: EQUATE: A benchmark evaluation framework for quantitative reasoning in natural language inference. In: CoNLL, pp. 349\u2013361 (2019). https:\/\/doi.org\/10.18653\/v1\/K19-1033","DOI":"10.18653\/v1\/K19-1033"},{"key":"14_CR25","unstructured":"Roller, S., Speriosu, M., Rallapalli, S., Wing, B., Baldridge, J.: Supervised text-based geolocation using language models on an adaptive grid. In: EMNLP, pp. 1500\u20131510 (2012). https:\/\/aclanthology.org\/D12-1137\/"},{"key":"14_CR26","doi-asserted-by":"publisher","unstructured":"Rybak, P., Mroczkowski, R., Tracz, J., Gawlik, I.: KLEJ: comprehensive benchmark for polish language understanding. In: ACL, pp. 1191\u20131201 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.111","DOI":"10.18653\/v1\/2020.acl-main.111"},{"key":"14_CR27","doi-asserted-by":"publisher","unstructured":"Shavrina, T., et al.: Russiansuperglue: a Russian language understanding evaluation benchmark. In: EMNLP, pp. 4717\u20134726 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.381","DOI":"10.18653\/v1\/2020.emnlp-main.381"},{"key":"14_CR28","unstructured":"Wang, A., et al.: Superglue: a stickier benchmark for general-purpose language understanding systems. In: NeurIPS, pp. 3261\u20133275 (2019). https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/4496bf24afe7fab6f046bf4923da8de6-Abstract.html"},{"key":"14_CR29","unstructured":"Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: GLUE: A multi-task benchmark and analysis platform for natural language understanding. In: ICLR (2019). https:\/\/openreview.net\/forum?id=rJ4km2R5t7"},{"key":"14_CR30","unstructured":"Wang, L.L., et al.: CORD-19: the covid-19 open research dataset. CoRR abs\/2004.10706 (2020). https:\/\/arxiv.org\/abs\/2004.10706"},{"key":"14_CR31","unstructured":"Wang, W., et al.: Structbert: Incorporating language structures into pre-training for deep language understanding. In: ICLR (2020). https:\/\/openreview.net\/forum?id=BJgQ4lSFPH"},{"key":"14_CR32","unstructured":"Wei, J., et al.: NEZHA: neural contextualized representation for Chinese language understanding abs\/1909.00204 (2019). http:\/\/arxiv.org\/abs\/1909.00204"},{"key":"14_CR33","doi-asserted-by":"crossref","unstructured":"Wilie, B., et al.: Indonlu: benchmark and resources for evaluating indonesian natural language understanding. In: ACL, pp. 843\u2013857 (2020). https:\/\/doi.org\/2020.aacl-main.85\/","DOI":"10.18653\/v1\/2020.aacl-main.85"},{"key":"14_CR34","doi-asserted-by":"publisher","unstructured":"Xu, L., et al.: CLUE: a Chinese language understanding evaluation benchmark. In: COLING, pp. 4762\u20134772 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.coling-main.419","DOI":"10.18653\/v1\/2020.coling-main.419"},{"key":"14_CR35","doi-asserted-by":"publisher","unstructured":"Zhang, N., et al.: CBLUE: a Chinese biomedical language understanding evaluation benchmark. In: ACL, pp. 7888\u20137915 (2022). https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.544","DOI":"10.18653\/v1\/2022.acl-long.544"},{"key":"14_CR36","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Han, X., Liu, Z., Jiang, X., Sun, M., Liu, Q.: ERNIE: enhanced language representation with informative entities. In: ACL, pp. 1441\u20131451 (2019). https:\/\/doi.org\/10.18653\/v1\/p19-1139","DOI":"10.18653\/v1\/p19-1139"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0847-8_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T05:05:36Z","timestamp":1734066336000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0847-8_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,14]]},"ISBN":["9789819608461","9789819608478"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0847-8_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,14]]},"assertion":[{"value":"14 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}