Geotagging Text Data on the Web—A Geometrical Approach

Radke, Mansi A.; Gautam, Nitin; Tambi, Akhil; Deshpande, Umesh; Syed, Zareen

doi:10.1109/access.2018.2843814

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Several techniques were explored to extract and refine location mentions in text such as geoparsing, location disambiguation, and geotagging [22]. For instance, geotagging text data on the web has been approached through geometrical methods [26], offering an alternative to relying solely on explicit geotags. The potential of Twitter data were highlighted both in determining the geographic origin of user-generated content [12], and developing predictive models that can estimate the location of Twitter users based on their posted content [39].…”

Section: Related Literaturementioning

confidence: 99%

Leave no Place Behind: Improved Geolocation in Humanitarian Documents

Belliardo,

Kalimeri,

Mejova

2023

Proceedings of the 2023 ACM Conference on Information Technology for Social Good

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Geographical location is a crucial element of humanitarian response, outlining vulnerable populations, ongoing events, and available resources. Latest developments in Natural Language Processing may help in extracting vital information from the deluge of reports and documents produced by the humanitarian sector. However, the performance and biases of existing state-of-the-art information extraction tools are unknown. In this work, we develop annotated resources to fine-tune the popular Named Entity Recognition (NER) tools Spacy and roBERTa to perform geotagging of humanitarian texts. We then propose a geocoding method FeatureRank which links the candidate locations to the GeoNames database. We find that not only does the humanitarian-domain data improves the performance of the classifiers (up to F1 = 0.92), but it also alleviates some of the bias of the existing tools, which erroneously favor locations in the Western countries. Thus, we conclude that more resources from non-Western documents are necessary to ensure that off-the-shelf NER systems are suitable for the deployment in the humanitarian sector. CCS CONCEPTS• Social and professional topics → Geographic characteristics; • Information systems → Data mining; Information systems applications.

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Section: Related Literaturementioning

confidence: 99%

Leave no Place Behind: Improved Geolocation in Humanitarian Documents

Belliardo,

Kalimeri,

Mejova

2023

Proceedings of the 2023 ACM Conference on Information Technology for Social Good

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“…Geotagging is the important activity to generate POI data and many researchers worked in this domain [36][37][38][39]. Some authors suggested the techniques to inference the geographical location based on text, image and categories [40][41][42][43][44][45][46][47]. Authors explained that even incomplete data can also be useful hence we divided the dataset into four classes and proposed a method to categorize the POI in a suitable class [48].…”

Section: Review Summarymentioning

confidence: 99%

Fine-tuned Predictive Model for Verifying POI Data

Monika¹,

Bundele²,

Bothale³

et al. 2021

IJACSA

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Mapping websites and geo portals are playing a vital role in daily life due to the availability of geo-tagged data. From booking a cab to search a place, getting traffic information, review of the place, searching for a doctor or best school available in the locality, we are heavily dependent on the map services and geo portals available for finding such information. There is voluminous data available on these sources and it is getting increasing every moment. These data are majorly collected through crowdsourcing methods where people are contributing. As a basic principle of Garbage in garbage out, the quality of this data impacts the quality of the services based on this data. Therefore, it is highly desired to have a model which can predict the quality/accuracy of the geotagged Point of interest data. We propose a novel Fine-Tuned Predictive Model to check the accuracy of this data using the best suitable supervised machine learning approach. This work focuses on the complete life cycle of the model building, starting from the data collection to the fine-tuning of the hyperparameters. We covered the challenges particularly to the geotagged POI data and remedies to resolve the issues to make it suitable for predictive modeling for classifying the data based on their accuracy. This is a unique work that considers multiple sources including ground truth data to verify the geotagged data using a machine learning approach. After exhaustive experiments, we obtained the best values for hyperparameters for the selected predictive model built on the real data set prepared specifically to target the proposed solution. This work provides a way to develop a robust pipeline for predicting the accuracy of crowdsourced geotagged data.

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“…This method does not consider toponym disambiguation. In this line, Radke et al [8] proposed an algorithm for geographical labeling of web documents considering all place names without solving possible ambiguities between them. Woodruff et al [9] developed a method that automatically extracts the words and phrases (only in English) that contain names of geographical places and assigns their most likely coordinates.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Geoparsing Method for Toponym Recognition and Resolution in Unstructured Text

et al. 2020

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The automatic extraction of geospatial information is an important aspect of data mining. Computer systems capable of discovering geographic information from natural language involve a complex process called geoparsing, which includes two important tasks: geographic entity recognition and toponym resolution. The first task could be approached through a machine learning approach, in which case a model is trained to recognize a sequence of characters (words) corresponding to geographic entities. The second task consists of assigning such entities to their most likely coordinates. Frequently, the latter process involves solving referential ambiguities. In this paper, we propose an extensible geoparsing approach including geographic entity recognition based on a neural network model and disambiguation based on what we have called dynamic context disambiguation. Once place names are recognized in an input text, they are solved using a grammar, in which a set of rules specifies how ambiguities could be solved, in a similar way to that which a person would utilize, considering the context. As a result, we have an assignment of the most likely geographic properties of the recognized places. We propose an assessment measure based on a ranking of closeness relative to the predicted and actual locations of a place name. Regarding this measure, our method outperforms OpenStreetMap Nominatim. We include other assessment measures to assess the recognition ability of place names and the prediction of what we called geographic levels (administrative jurisdiction of places).

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Geotagging Text Data on the Web—A Geometrical Approach

Cited by 7 publications

References 14 publications

Leave no Place Behind: Improved Geolocation in Humanitarian Documents

Leave no Place Behind: Improved Geolocation in Humanitarian Documents

Fine-tuned Predictive Model for Verifying POI Data

Adaptive Geoparsing Method for Toponym Recognition and Resolution in Unstructured Text

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