Detecting geo-relation phrases from web texts for triplet extraction of geographic knowledge: a context-enhanced method

References

• Bengio, Y., Ducharme, R., Vincent, P., & Janvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3, 1137–1155.
   Web of Science ®Google Scholar
• Blessing, A., & Schütze, H. (2010). Self-annotation for fine-grained geospatial relation extraction. In Proceedings of the 23rd international conference on computational linguistics (pp. 80–88). Beijing, China.
   Google Scholar
• Cao, C., Wang, S., & Jiang, L. (2014). A practical approach to extracting names of geographical entities and their relations from the web. In International conference on knowledge science, engineering and management (pp. 210–221). Sibiu, Romania.
   Google Scholar
• Chen, J., Ji, D., Tan, C. L., & Niu, Z. (2005). Unsupervised feature selection for relation extraction. In Proceedings of the 2nd international joint conference on natural language processing (pp. 262–267). Jeju Island, Korea.
   Google Scholar
• Dash, M., Choi, K., Scheuermann, P., & Liu, H. (2002). Feature selection for clustering-a filter solution. In Proceedings of international conference on data mining (pp. 115–122). Maebashi, Japan.
   Google Scholar
• Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019) (pp. 4171–4186), Minneapolis, MN.
   Google Scholar
• Elia, A., Guglielmo, D., Maisto, A., & Pelosi, S. (2013). A linguistic-based method for automatically extracting spatial relations from large non-structured data. In Proceedings of the 13th international conference on algorithms and architectures for parallel processing (pp. 193–200). Vietri sul Mare, Italy.
   Google Scholar
• Hasegawa, T., Sekine, S., & Grishman, R. (2004). Discovering relations among named entities from large corpora. In Proceedings of the 42nd annual meeting on association for computational linguistics (p. 415). Barcelona, Spain.
   Google Scholar
• Jin, Y., Zhao, S., & Wu, Y. (2019). Geographic entity relationship extraction model based on piecewise convolution of residual network. In the 3rd international conference on machine learning and soft computing (ICMLSC 2019) (pp. 160–165). Da Lat, Viet Nam.
   Google Scholar
• Khan, A., Vasardani, M., & Winter, S. (2013). Extracting spatial information from place descriptions. In Proceedings of the first ACM SIGSPATIAL international workshop on computational models of place (p. 62). Orlando, Florida.
   Google Scholar
• Li, W., Goodchild, M. F., & Raskin, R. (2014). Towards geospatial semantic search: exploiting latent semantic relations in geospatial data. International Journal of Digital Earth, 7(1), 17–37.
   Web of Science ®Google Scholar
• Liu, K., Gao, S., Qiu, P., Liu, X., Yan, B., & Lu, F. (2017). Road2Vec: Measuring traffic interactions in urban road system from massive travel routes. ISPRS International Journal of Geo-Information, 6(11), 321.
   Web of Science ®Google Scholar
• Loglisci, C., Ienco, D., Roche, M., Teisseire, M., & Malerba, D. (2012). Toward geographic information harvesting: Extraction of spatial relational facts from web documents. In 2012 IEEE 12th international conference on data mining workshops (pp. 789–796). Brussels, Belgium.
   Google Scholar
• Mai, G., Yan, B., Janowicz, K., & Zhu, R. (2019). Relaxing unanswerable geographic questions using a spatially explicit knowledge graph embedding model. In the 22nd AGILE Conference on Geographic Information Science (AGILE 2019) (pp. 21–39). Limassol, Cyprus.
   Google Scholar
• Mesquita, F. (2012). Clustering techniques for open relation extraction. In Proceedings of the SIGMOD/PODS 2012 PhD symposium (pp. 27–32). New York, USA.
   Google Scholar
• Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In the Workshop at International Conference on Learning Representations 2013 (pp. 1–12). Scottsdale, Arizona, USA.
   Google Scholar
• Mintz, M., Bills, S., Snow, R., & Jurafsky, D. (2009). Distant supervision for relation extraction without labeled data. In the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP (ACL ‘09) (pp. 1003–1011). Suntec, Singapore.
   Google Scholar
• Mirrezaei, S. I., Martins, B., & Cruz, I. F. (2016). A distantly supervised method for extracting spatio-temporal information from text. In the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPACIAL ‘16) (pp. 74: 1–74:4). Burlingame, California.
   Google Scholar
• Naughton, M., Stokes, N., & Carthy, J. (2010). Sentence-level event classification in unstructured texts. Information Retrieval, 13(2), 132–156.
   Web of Science ®Google Scholar
• Pershina, M., Min, B., Xu, W., & Grishman, R. (2014). Infusion of labelled data into distant supervision for relation extraction. In Proceedings of the 52nd annual meeting of the association for computational linguistics (pp. 732–738). Baltimore, MD.
   Google Scholar
• Quan, C., Wang, M., & Ren, F. (2014). An unsupervised text mining method for relation extraction from biomedical literature. PloS ONE, 9(7), e102039.
   Web of Science ®Google Scholar
• Schmitz, M., Bart, R., Soderland, S., & Etzioni, O. (2012). Open language learning for information extraction. In Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning (pp. 523–534). Jeju Island, Korea.
   Google Scholar
• Shen, M., Liu, D. R., & Huang, Y. S. (2012). Extracting semantic relations to enrich domain ontologies. Journal of Intelligent Information Systems, 39(3), 749–761.
   Web of Science ®Google Scholar
• Smole, D., Čeh, M., & Podobnikar, T. (2011). Evaluation of inductive logic programming for information extraction from natural language texts to support spatial data recommendation services. International Journal of Geographical Information Science, 25(11), 1809–1827.
   Web of Science ®Google Scholar
• Stefanakis, E. (2003). Modelling the history of semi-structured geographical entities. International Journal of Geographical Information Science, 17(6), 517–546.
   Web of Science ®Google Scholar
• Xiong, S., Mao, J., Duan, P., & Miao, S. (2017). Chinese geographical knowledge entity relation extraction via deep neural networks. In the 9th international conference on agents and artificial intelligence (ICAART 2017) (pp. 24–33). Porto, Portugal.
   Google Scholar
• Yan, Y., Okazaki, N., Matsuo, Y., Yang, Z., & Ishizuka, M. (2009). Unsupervised relation extraction by mining Wikipedia texts using information from the web. In Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP: Volume 2-Volume 2 (pp. 1021–1029). Suntec, Singapore.
   Google Scholar
• Zhang, P., Li, W., Hou, Y., & Song, D. (2011). Developing position structure-based framework for Chinese entity relation extraction. ACM Transactions on Asian Language Information Processing (TALIP), 10(3), 14.
   Google Scholar
• Zhang, W., Sun, L., & Zhang, X. (2012). An entity relation extraction method based on wikipedia and pattern clustering. Journal of Chinese Information Processing, 26(2), 75–81.
   Google Scholar
• Zhu, Y., Zhu, A.-X., Feng, M., Song, J., Zhao, H., Yang, J., … Yao, L. (2017a). A similarity-based automatic data recommendation approach for geographic models. International Journal of Geographical Information Science, 31(7), 1403–1424.
   Web of Science ®Google Scholar
• Zhu, Y., Zhu, A.-X., Song, J., Yang, J., Feng, M., Sun, K., … Zhao, H. (2017b). Multidimensional and quantitative interlinking approach for linked geospatial data. International Journal of Digital Earth, 10(9), 923–943.
   Web of Science ®Google Scholar