Automated Extraction of Geographic Locations from Natural Language Text: Implications for Process Control and Mechanism

Context: Natural Language Processing (NLP) techniques, along with database management tools and fuzzy string-matching libraries, play a pivotal role in automating the extraction of geographic locations from textual data. They can be applied in various fields such as geographic information systems and data mining, facilitating efficient data analysis and decision-making. Objective: This article presents a comprehensive approach for the automated extraction of geographic locations from natural language text. The primary objective is to utilize NLP techniques, including Named Entity Recognition, in conjunction with database management, using SQLite3 and fuzzy string matching with the FuzzyWuzzy library, to accurately identify and extract place names from textual data. Additionally, this study aims to investigate this approach in decision-making processes. Methods: Our methodology integrates NLP techniques, SQLite3 for database management, and the FuzzyWuzzy library for fuzzy string matching. Initially, NLP techniques, particularly NER, are employed to identify potential place names within the text. Subsequently, the identified entities are stored and managed in a SQLite3 database, enabling efficient retrieval and organization of geographic information. Finally, the FuzzyWuzzy library is utilized for fuzzy string matching to ensure accurate matching of extracted entities with known geographic locations. Results: Our approach has been validated against existing datasets and benchmarks, demonstrating high accuracy and precision in geographic location extraction. Performance metrics such as precision, recall, and F1 score have been calculated to assess the effectiveness of our method. The methodology has shown promising results, achieving robust performance in identifying and extracting place names from textual data. Conclusion: The automated extraction of geographic locations from natural language text holds significant implications for various sectors, including geographic information systems, travel planning, urban development, and disaster response. By streamlining workflows and improving the accuracy of geographic data analysis, our methodology contributes to enhancing decision-making processes and improving the efficiency of tasks reliant on geographic information