A HYBRID QUANTUM-PERFECTED MODEL OF ARTIFICIAL INTELLIGENCE IN THE PROBLEM OF AUTOMATIC RECOGNITION AND FAST CONVERSION OF UNSTRUCTURED TEXT INFORMATION INTO SPATIAL
DOI:
https://doi.org/10.17721/AIT.2023.1.06Keywords:
QNLP, hybrid quantum-classical neural networks, geocoding, geoparsing, geographic information systems, ArcGIS, entity recognition, unstructured textual information, variational quantum igniter, QLSTM, spatial objects.Abstract
Abstract DOI: https://doi.org/10.17721/AIT.2023.1.06 Background. Efficiently converting large amounts of unstructured text data into spatial information is crucial for managing water distribution systems. This allows for the conversion of extensive sets of text information, such as reports, orders, letters, and other documents, into point classes of spatial objects in geographic information systems. To tackle this challenge, a promising new approach involves combining hybrid quantum-classical neural networks with geo-information technologies. Methods. The study utilized quantum-enhanced hybrid neural networks in combination with GIS methods to identify named entities such as personal accounts and balance sheet objects of Kyivvodokanal by their addresses and geocoding. This information was then published on a geoportal using the ArcGIS Enterprise platform in real-time, which holds great promise for effective water management. The performance of the developed model was evaluated by accuracy indicators, recall parameters, and weighted harmonic average of accuracy and recall. Results. The obtained results indicate that the developed hybrid quantum-classical model of artificial intelligence can be successfully applied to transform large volumes of unstructured textual information into spatial information. The model was integrated into GIS using ArcGIS Enterprise. By combining the obtained point classes of spatial objects with already existing data, methods of spatial connections, an interactive map with an update interval of every five minutes was developed. Conclusions. Taking advantage of quantum computing and combining it with classical hardware and classical AI models, it became possible to achieve similar and even better performance in various tasks compared to state-of-the-art methods. Quantum natural language processing is a promising new field that has the potential to revolutionize the way one analyzes and understands human language.Downloads
References
Airola, A., Pohjankukka, J., Torppa, J., Middleton, M., Nykänen, V., Heikkonen, J., & Pahikkala, T. (2019). The spatial leave-pair-out cross-validation method for reliable AUC estimation of spatial classifiers. Data Mining and Knowledge Discovery, 33(3), 730–747.
Andrade, F. G., Carvalho-Ramalho, R. E., Firmino, A. A., Souza-Baptista, C., Ramos-Falcao, A. G., & Oliveira, M. G. (2020). Using Natural Language Processing for Extracting GeoSpatial Urban Issues Complaints from TV News. GEOProcessing 2020: The Twelfth International Conference on Advanced Geographic Information Systems, Applications, and Services. International Academy, Research, and Industry Association.
ArcGIS 2023. https://developers.arcgis.com/python/guide/how-named-entity-recognition-works/
ArcGIS API for Python. https://developers.arcgis.com/python/guide/how-named-entity-recognition-works/
Baidu. Geocoding API v2.0. https://api.map.baidu.com/lbsapi/cloud/webservice-geocoding.htm
Bhagvan, K. (2020). Quantum Computing Solutions: Solving Real-World Problems Using Quantum Computing and Algorithms. Apress.
Chen, S. (2022). Quantum long short-term memory. arXiv:2009.01783. doi: https://doi.org/10.48550/arXiv.2009.01783
De Bruijn, J. A., de Moel, H., Jongman, B., de Ruiter, M. C., Wagemaker, J., & Aerts, J.C. (2019). A global database of historic and real-time flood events based on social media. Sci. Data.
DisCoCat lambeq. https://cqcl.github.io/lambeq/tutorials/discocat.html
Doccano. https://github.com/doccano/doccano
Enkhsaikhan, M., Holden, E.-J., Duuring, P., & Liu, W. (2021). Understanding ore-forming conditions using machine reading of text. Ore Geology Reviews.
Enkhsaikhan, M., Liu, W., Holden, E.-J., & Duuring, P. (2021). Auto-labelling entities in low-resource text: A geological case study. Knowledge and Information Systems, 63(3), 695–715.
Floridi, L., & Chiriatti, M. (2020). GPT-3: Its Nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694.
Gonzlez-Castillo, S., & Combarro, E., F. (2023). A Practical Guide to Quantum Machine Learning and Quantum Optimization. Packt Publishing.
Guarasci, R., De Pietro, G., & Esposito, M. (2022). Quantum natural language processing: Challenges and opportunities. Applied Sciences.
Hoffmann, T. (2021). Quantum Models for WordSense Disambiguation. Master’s thesis in Complex Adaptive Systems. Chalmers University of Technology.
Hu, Y., Mao, H., & McKenzie, G. (2019). A natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements. Int. J. Geogr. Inf. Sci., (33), 714–738.
Huang, Z., Xu, W., & Yu, K. (2015). Bidirectional LSTM-CRF Models for Sequence Tagging. arXiv:1508.01991v1. doi: https://doi.org/10.48550/arXiv.1508.01991
Jacquier, A., Kondratyev, O., Lipton, A., & López de Prado, M. (2022). Quantum Machine Learning and Optimisation in Finance. Packt Publishing.
Lai, J., Lansley, G., Haworth, J., & Cheng, T. (2020). A name-led approach to profile urban places based on geotagged Twitter data. Trans. GIS 2020, (24), 858–879.
Lawley, C., Gadd, M. G., Parsa, M., Lederer, G. W., Graham, G. E. & Ford, A. (2023). Applications of Natural Language Processing to Geoscience Text Data and Prospectivity Modeling. Natural Resources Research. Springer.
Ma, K., Tian, M., Tan, Y., Xie, X., & Qiu, Q. (2021). What is this article about? Generative summarization with the BERT model in the geosciences domain. Earth Science Informatics.
McKenzie, G., Liu, Z., Hu, Y., & Lee, M. (2018). Identifying urban neighborhood names through user-contributed online property listings. ISPRS Int. J. Geo-Inf., (7), 388.
O’Riordan, L. J., Doyle, M., Baruffa, F., & Kannan, V. (2020). A hybrid classical-quantum workflow for natural language processing. arXiv:2004.06800. doi: https://doi.org/10.48550/arXiv.2004.06800.
Peixeiro, M. (2022). Time Series Forecasting in Python. Manning.
PennyLane (2023). https://pennylane.ai/qml/
Qiskit 2023. https://qiskit.org/ecosystem/machine-learning/tutorials/01_neural_networks.html
Rieffel, E. G., & Polak, W. H. (2011). Quantum Computing: A Gentle Introduction. MIT Press: Cambridge, MA, USA.
Scikit-learn. https://scikit-learn.org/stable/index.html
spaCy. Industrial-Strength Natural Language Processing in Python. https://spacy.io/
The Stanford Natural Language Processing Group. Stanford Named Entity Recognizer (NER). https://nlp.stanford.edu/software/CRF-NER.shtml
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., N, Kaiser Ł., & Polosukhin, I. (2017). Attention Is All You Need. 31st Conference on Neural Information Processing Systems (NIPS 2017). Long Beach, CA, USA.
Wang, S., Yan X., Zhu, Y., Song, J., Sun, K., Li, W., Hu, L., Qi, Y., & Xu, H. (2022). New Era for Geo-Parsing to Obtain Actual Locations: A Novel Toponym Correction Method Based on Remote Sensing Images. Remote Sensing. Special Issue “Intelligent Perception in Urban Spaces from Photogrammetry and Remote Sensing“, 14(19), 4725. https://doi.org/10.3390/rs14194725/
Wu, Y., & Wang, Q. (2019). A Categorical Compositional Distributional Modelling for the Language of Life. arXiv:1902.09303. https://doi.org/10.48550/arXiv.1902.0930 Zhu, D., Linke, N.M., Benedetti, M., Landsman, K.A., Nguyen, N.H., Alderete, C.H., Perdomo-Ortiz, A., Korda, N., Garfoot, A., & Brecque, C. (2019). Training of quantum circuits on a hybrid quantum computer. Science Advances. doi: https://doi.org/10.1126/sciadv.aaw9918
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