A machine learning approach to extracting spatial information from geological texts in Chinese

References

• Alimova, I., and Tutubalina, E., 2020. Multiple features for clinical relation extraction: a machine learning approach. Journal of Biomedical Informatics, 103, 103382.
   PubMed Web of Science ®Google Scholar
• Bollegala, D.T., Matsuo, Y., and Ishizuka, M., 2010. Relational duality: Unsupervised extraction of semantic relations between entities on the web. In: Proceedings of the 19th international conference on World Wide Web, 151–160. New York, NY: Association for Computing Machinery.
   Google Scholar
• Bouziat, A., et al., 2020. Assisted processing of geological data with Deep Learning technologies: levers to optimize mineral exploration workflows? Mineral Exploration Symposium. European Association of Geoscientists & Engineers, 2020 (1), 1–4.
   Google Scholar
• Brin, S., 1999. Extracting patterns and relations from the World Wide Web. In: P. Atzeni, A. Mendelzon, and G. Mecca, eds. The World Wide Web and Databases. WebDB 1998. Lecture Notes in Computer Science, vol. 1590. Berlin: Springer, 172–183. https://doi.org/10.1007/10704656_11
   Google Scholar
• Califf, M.E., and Mooney, R.J., 2003. Bottom-up relational learning of pattern matching rules for information extraction. Journal of Machine Learning Research, 4 (2), 177–210.
   Web of Science ®Google Scholar
• Chen, N., et al., 2021. KE-CNN: A new social sensing method for extracting geographical attributes from text semantic features and its application in Wuhan, China. Computers, Environment and Urban Systems, 88, 101629.
   Web of Science ®Google Scholar
• Chen, P., et al., 2019. STLP-GSM: a method to predict future locations of individuals based on geotagged social media data. International Journal of Geographical Information Science, 33 (12), 2337–2362.
   Web of Science ®Google Scholar
• Chu, D.P., et al., 2021. Geological entity recognition based on ELMO-CNN-BiLSTM-CRF model. Earth Science-Journal of China University of Geosciences, 46 (8), 3039–3048.
   Google Scholar
• Church, K.W., 2017. Word2Vec. Natural Language Engineering, 23 (1), 155–162.
   Web of Science ®Google Scholar
• Derungs, C., and Samardžić, T., 2018. Are prominent mountains frequently mentioned in text? Exploring the spatial expressiveness of text frequency. International Journal of Geographical Information Science, 32 (5), 856–873.
   Web of Science ®Google Scholar
• Devlin, J., et al., 2018. BERT: pre-training of deep bidirectional transformers for language understanding. NAACL-HLT, 1: 4171–4186.
   Google Scholar
• Du, S., and Guo, L., 2016. Similarity measurements on multi-scale qualitative locations. Transactions in GIS, 20 (6), 824–847.
   Web of Science ®Google Scholar
• Du, S., et al., 2017. Classifying natural-language spatial relation terms with random forest algorithm. International Journal of Geographical Information Science, 31 (3), 542–568.
   Web of Science ®Google Scholar
• Du, S., Feng, C.-C., and Guo, L., 2015. Integrative representation and inference of qualitative locations about points, lines, and polygons. International Journal of Geographical Information Science, 29 (6), 980–1006.
   Web of Science ®Google Scholar
• Egenhofer, M.J., 1991. Reasoning about binary topological relations. In: O. Günther, H.J. Schek, eds. Advances in spatial databases. SSD 1991. Lecture Notes in Computer Science, vol. 525. Berlin, Heidelberg: Springer, 141–160. https://doi.org/10.1007/3-540-54414-3_36
   Google Scholar
• Egenhofer, M.J., and Mark, D.M., 1995. Naive geography. In: A.U. Frank and W. Kuhn, eds. Spatial information theory: a theoretical basis for GIS, lecture notes in computer sciences 988. Berlin: Springer-Verlag, 1–15.
   Google Scholar
• Elsahar, H., et al., 2017. Unsupervised open relation extraction. In: E. Blomqvist, K. Hose, H. Paulheim, A. Ławrynowicz, F. Ciravegna, O. Hartig, eds. The Semantic Web: ESWC 2017 Satellite Events. ESWC 2017. Lecture Notes in Computer Science, vol. 10577. Cham: Springer. https://doi.org/10.1007/978-3-319-70407-4_3
   Google Scholar
• Estak, M., et al., 2021. Applying k -vertex cardinality constraints on a Neo4j graph database. Future Generation Computer Systems, 115, 459–474.
   Web of Science ®Google Scholar
• Etzioni, O., et al., 2008. Open information extraction from the web. Communications of the ACM, 51 (12), 68–74.
   Web of Science ®Google Scholar
• Geng, B., 2021. Open relation extraction in patent claims with a hybrid network. Wireless Communications and Mobile Computing, 2021 (1), 1–7.
   PubMedGoogle Scholar
• Geng, Z.Q., Zhang, Y.H., and Han, Y.M., 2021. Joint entity and relation extraction model based on rich semantics. Neurocomputing, 429, 132–140.
   Web of Science ®Google Scholar
• Haris, E., Gan, K.H., and Tan, T.-P., 2020. Spatial information extraction from travel narratives: analysing the notion of co-occurrence indicating closeness of tourist places. Journal of Information Science, 46 (5), 581–599.
   Web of Science ®Google Scholar
• He, C., et al., 2020. Open domain Chinese triples hierarchical extraction method. Applied Sciences, 10 (14), 4819.
   Google Scholar
• Hou, J., et al., 2020. BERT-based Chinese relation extraction for public security. IEEE Access., 8, 132367–132375.
   Web of Science ®Google Scholar
• Hu, Y., 2018a. Geospatial semantics. In: B. Huang, ed. Comprehensive geographic information systems. Oxford: Elsevier, 80–94.
   Google Scholar
• Hu, Y., 2018b. Geo-text data and data-driven geospatial semantics. Geography Compass, 12 (11), e12404.
   Web of Science ®Google Scholar
• Hu, Y., Ye, X., and Shaw, S.-L., 2017. Extracting and analyzing semantic relatedness between cities using news articles. International journal of geographical information science, 31 (12), 2427–2451.
   Web of Science ®Google Scholar
• Huang, H., Lei, M., and Feng, C., 2021. Graph-based reasoning model for multiple relation extraction. Neurocomputing, 420, 162–170.
   Web of Science ®Google Scholar
• Huang, T., et al., 2019. Unsupervised monocular depth estimation based on residual neural network of coarse–refined feature extractions for drone. Electronics, 8 (10), 1179.
   Web of Science ®Google Scholar
• Huang, Y., et al., 2022. A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning. Frontiers of Environmental Science & Engineering, 16 (3), 1–12.
   Web of Science ®Google Scholar
• Janowicz, K., et al., 2020. GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, 34 (4), 625–636.
   Web of Science ®Google Scholar
• Kim, J.-T., and Moldovan, D.I., 1995. Acquisition of linguistic patterns for knowledge-based information extraction. IEEE Transactions on Knowledge and Data Engineering, 7 (5), 713–724.
   Web of Science ®Google Scholar
• Kim, J.-H., 2009. Estimating classification error rate: repeated cross-validation, repeated hold-out and bootstrap. Computational Statistics & Data Analysis, 53 (11), 3735–3745.
   Web of Science ®Google Scholar
• Kordjamshidi, P., Pustejovsky, J., and Moens, M.F., 2020. Representation, learning and reasoning on spatial language for downstream NLP task. In: Proceedings of the 2020 conference on empirical methods in natural language processing: tutorial abstracts, 28–33, Stroudsburg, PA. Association for Computational Linguistics.
   Google Scholar
• Laurini, R., 2015. Geographic ontologies, gazetteers and multilingualism. Future Internet, 7 (4), 1–23.
   Google Scholar
• Lin, Y.K., et al., 2016. Neural relation extraction with selective attention over instances. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL, 2124–2133. Stroudsburg, PA: Association for Computational Linguistics.
   Google Scholar
• Liu, W., et al., 2020. A multi-label text classification model based on ELMo and attention. MATEC Web of Conferences, 309, 03015.
   Google Scholar
• Liu, Y., et al., 2021. Quality assessment of post-consumer plastic bottles with joint entropy method: A case study in Beijing, China. Resources, Conservation and Recycling, 175, 105839.
   Web of Science ®Google Scholar
• Liu, Z., et al., 2015. Automatic de-identification of electronic medical records using token-level and character-level conditional random fields. Journal of Biomedical Informatics, 58, S47–S52.
   PubMed Web of Science ®Google Scholar
• Luo, X., et al., 2018. Attention-based relation extraction with bidirectional gated recurrent unit and highway network in the analysis of geological data. IEEE Access., 6, 5705–5715.
   Web of Science ®Google Scholar
• Ma, M., et al., 2019. Flash flood risk analysis based on machine learning techniques in the Yunnan Province, China. Remote Sensing, 11 (2), 170.
   Web of Science ®Google Scholar
• Ma, X., and Hovy, E., 2016. End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: Proceedings of the 54th annual meeting of the association for computational linguistics (Volume 1: Long Papers), Berlin, Germany, 1064–1074. Stroudsburg, PA: Association for Computational Linguistics.
   Google Scholar
• McDonough, K., et al., 2019. Named entity recognition goes to old regime France: geographic text analysis for early modern French corpora. International Journal of Geographical Information Science, 33 (12), 2498–2522.
   Web of Science ®Google Scholar
• Nazar, R., Vivaldi, J., and Wanner, L., 2012. Automatic taxonomy extraction for specialized domains using distributional semantics. Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication, 18 (2), 188–225.
   Google Scholar
• Nesi, P., Pantaleo, G., and Tenti, M., 2016. Geographical localization of web domains and organization addresses recognition by employing natural language processing, pattern matching and clustering. Engineering Applications of Artificial Intelligence, 51, 202–211.
   Web of Science ®Google Scholar
• None, 1993. Bootstrapping: a nonparametric approach to statistical inference. Technometrics, 36 (4), 435–436.
   Google Scholar
• Pang, N., et al., 2020. Domain relation extraction from noisy Chinese texts. Neurocomputing, 418, 21–35.
   Web of Science ®Google Scholar
• Papadias, D., and Theodoridis, Y., 1997. Spatial relations, minimum bounding rectangles, and spatial data structures. International Journal of Geographical Information Science, 11 (2), 111–138.
   Web of Science ®Google Scholar
• Purves, R.S., et al., 2018. Geographic information retrieval: progress and challenges in spatial search of text. Foundations and Trends® in Information Retrieval, 12 (2–3), 164–318.
   Web of Science ®Google Scholar
• Qiu, Q., et al., 2019a. BiLSTM-CRF for geological named entity recognition from the geoscience literature. Earth Science Informatics, 12 (4), 565–579.
   Web of Science ®Google Scholar
• Qiu, Q., et al., 2019b. GNER: a generative model for geological named entity recognition without labeled data using deep learning. Earth and Space Science, 6 (6), 931–946.
   Web of Science ®Google Scholar
• Qiu, Q., et al., 2020. Automatic spatiotemporal and semantic information extraction from unstructured geoscience reports using text mining techniques. Earth Science Informatics, 13 (4), 1393–1410.
   Web of Science ®Google Scholar
• Reinbergerr, M.-L., 2005. Automatic extraction of spatial relations. In 2005 Purtuguese CONFERENce on artificial intelligence, 331–337. Piscataway, NJ: IEEE. https://www.ieee.org/about/contact.html?utm_source=dhtml_footer&utm_medium=hp&utm_campaign=office-locations
   Google Scholar
• Schockaert, S., et al., 2008. Mining topological relations from the web. In: 2008 19th International Workshop on Database and Expert Systems Applications, 652–656. Piscataway, NJ: IEEE. https://doi.org/10.1109/DEXA.2008.15
   Google Scholar
• Shannon, E.C., 1948. A mathematical theory of communication. Bell System Technical Journal, 27 (3), 379–423.
   Google Scholar
• Shelmanov, A.O., et al., 2020. Open information extraction from texts: part II. extraction of semantic relations using unsupervised machine learning. Scientific and Technical Information Processing, 47 (6), 340–347.
   Web of Science ®Google Scholar
• Shi, W., 2008. Modeling uncertainty in geographic information and analysis. Science in China Series E: Technological Sciences, 51 (S1), 38–47.
   Google Scholar
• Shi, Y., et al., 2021. Distant Supervision Relation Extraction via adaptive dependency-path and additional knowledge graph supervision. Neural Networks : The Official Journal of the International Neural Network Society, 134, 42–53.
   PubMed Web of Science ®Google Scholar
• Stock, K., et al., 2022. Detecting geospatial location descriptions in natural language text. International Journal of Geographical Information Science, 36 (3), 547–584.
   Web of Science ®Google Scholar
• Tran, T.T., Le, P., and Ananiadou, S., 2020. Revisiting unsupervised relation extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 7498–7505, Stroudsburg, PA: Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.669
   Google Scholar
• Wang, C., et al., 2018. Information extraction and knowledge graph construction from geoscience literature. Computers & Geosciences, 112, 112–120.
   Web of Science ®Google Scholar
• Wang, M.Y., Li, L., and Huang, F., 2014. Semi-supervised Chinese open entity relation extraction. In: 2014 IEEE 3rd international conference on cloud computing and intelligence systems, 415–420. Piscataway, NJ: IEEE.
   Google Scholar
• Wang, W., and Stewart, K., 2015. Spatiotemporal and semantic information extraction from web news reports about natural hazards. Computers, Environment and Urban Systems, 50, 30–40.
   Web of Science ®Google Scholar
• Wartmann, F.M., Acheson, E., and Purves, R.S., 2018. Describing and comparing landscapes using tags, texts, and free lists: an interdisciplinary approach. International Journal of Geographical Information Science, 32 (8), 1572–1592.
   Web of Science ®Google Scholar
• Wellmann, J.F., and Regenauer-Lieb, K., 2012. Uncertainties have a meaning: Information entropy as a quality measure for 3-D geological models. Tectonophysics, 526–529, 207–216.
   Web of Science ®Google Scholar
• Wing, B.P., and Baldridge, J., 2011. Simple supervised document geolocation with geodesic grids. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 955–964. Stroudsburg, PA: Association for Computational Linguistics.
   Google Scholar
• Wu, L., et al., 2017. A knowledge-driven geospatially enabled framework for geological big data. ISPRS International Journal of Geo-Information, 6 (6), 166.
   Web of Science ®Google Scholar
• Xu, J., 2007. Formalizing natural‐language spatial relations between linear objects with topological and metric properties. International Journal of Geographical Information Science, 21 (4), 377–395.
   Web of Science ®Google Scholar
• Yang, S.M., Yoo, S.Y., and Jeong, O.R., 2020. DeNERT-KG: named entity and relation extraction model using DQN, knowledge graph, and BERT. Applied Sciences, 10 (18), 6429.
   Google Scholar
• Yu, H., et al., 2020. A relationship extraction method for domain knowledge graph construction. World Wide Web, 23 (2), 735–753.
   Google Scholar
• Yu, L., et al., 2016. Context enhanced keyword extraction for sparse geo-entity relation from web texts. In Asia-Pacific web conference, 253–264. Switzerland: Springer.
   Google Scholar
• Zenasni, S., et al., 2018. Spatial information extraction from short messages. Expert Systems with Applications, 95, 351–367.
   Web of Science ®Google Scholar
• Zhang, C., et al., 2015. Construction of semantic bootstrapping models for relation extraction. Knowledge-Based Systems, 83, 128–137.
   Web of Science ®Google Scholar
• Zhang, J., et al., 2020. A multi-feature fusion model for Chinese relation extraction with entity sense. Knowledge-Based Systems, 206, 106348.
   Web of Science ®Google Scholar
• Zhang, T., et al., 2021. Chinese medical relation extraction based on multi-hop self-attention mechanism. International Journal of Machine Learning and Cybernetics, 12 (2), 355–363.
   Web of Science ®Google Scholar
• Zhou, C., et al., 2021. Prospects for the research on geoscience knowledge graph in the big data era. Science China Earth Sciences, 64 (7), 1105–1114.,
   Web of Science ®Google Scholar
• Zuo, R., and Xiong, Y., 2020. Geodata science and geochemical mapping. Journal of Geochemical Exploration, 209, 106431.
   Web of Science ®Google Scholar
• Zuo, R., 2020. Geodata science-based mineral prospectivity mapping: a review. Natural Resources Research, 29 (6), 3415–3424.
   Web of Science ®Google Scholar