A Dataset and Evaluation Framework for Complex Geographical Description Parsing (2020.coling-main)
Copied to clipboard
| Challenge: | Previously, work on toponym resolution has focused on identifying and resolving individual toponyms in text like Adrano, S.Maria di Licodia or Catania. |
| Approach: | They propose a method that parses a set of coordinates and a collection of 360,187 uncurated complex geolocation descriptions to automate the process. |
| Outcome: | The proposed approach automates most of the process by combining Wikipedia and OpenStreetMap. |
Similar Papers
GeospaCy: A tool for extraction and geographical referencing of spatial expressions in textual data (2024.eacl-demo)
Copied to clipboard
| Challenge: | Spatial information in text enables to understand the geographical context and relationships within text for location-sensitive applications. |
| Approach: | They propose to use spatial information extracted from textual data to perform geoparsing and geocoding tasks. |
| Outcome: | The GeospaCy software tool is designed for the extraction and georeferencing of spatial information present in textual data. |
Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation (2024.findings-eacl)
Copied to clipboard
Shohei Higashiyama, Hiroki Ouchi, Hiroki Teranishi, Hiroyuki Otomo, Yusuke Ide, Aitaro Yamamoto, Hiroyuki Shindo, Yuki Matsuda, Shoko Wakamiya, Naoya Inoue, Ikuya Yamada, Taro Watanabe
| Challenge: | et al., 2006) considers geographic relatedness among geo-entity mentions in document-level geoparsing. |
| Approach: | They present a Japanese travelogue dataset that considers geographic relatedness among geo-entity mentions. |
| Outcome: | The proposed dataset includes 200 travelogue documents with rich geo-entity information . it shows that human activities, mobility, and events are often described with natural language expressions of locations or geographic entities (geo-entities) |
Automatic Construction of a Large-Scale Corpus for Geoparsing Using Wikipedia Hyperlinks (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to evaluate geoparsing systems are small-scale and lack coverage of location expressions on general domains. |
| Approach: | They propose a method to construct a large-scale corpus for geoparsing from Wikipedia articles. |
| Outcome: | The proposed method can annotate multiple location expressions with coordinates even with ambiguous expressions. |
Improving Toponym Resolution by Predicting Attributes to Constrain Geographical Ontology Entries (2024.naacl-short)
Copied to clipboard
| Challenge: | Existing approaches to geocoding only encode location mentions and their context . |
| Approach: | They propose a prompt-based approach to geocoding where the machine learning algorithm encodes only the location mention and its context. |
| Outcome: | The proposed model achieves state-of-the-art performance on multiple datasets. |
Coordinates from Context: Using LLMs to Ground Complex Location References (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing geocoding tools can only link locations already in a geographic database, which often do not include compositional locations. |
| Approach: | They propose a geocoding strategy that leverages LLMs' geospatial knowledge versus reasoning skills to improve performance for the task. |
| Outcome: | The proposed model improves performance and is comparable to larger models. |
Basreh or Basra? Geoparsing Historical Locations in the Svoboda Diaries (2024.acl-srw)
Copied to clipboard
| Challenge: | In the historical domain, many geoparsing corpora are from large news collections. |
| Approach: | They propose a pipeline employing named entity recognition for geotagging and a map-based generate-and-rank approach incorporating candidate name augmentation and clustering of location context words for geocoding. |
| Outcome: | The proposed pipeline outperforms existing map-based geoparsers in terms of accuracy, lowest mean distance error, and number of locations correctly identified. |
Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking (2024.eacl-long)
Copied to clipboard
| Challenge: | Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates. |
| Approach: | They propose a framework to integrate Chinese geographic semantics into re-ranking pipelines. |
| Outcome: | The proposed framework improves on two Chinese benchmark datasets. |
SpatialWebAgent: Leveraging Large Language Models for Automated Spatial Information Extraction and Map Grounding (2025.acl-demo)
Copied to clipboard
| Challenge: | Understanding and extracting spatial information from text is vital for a wide range of applications, says nielsen . inherent complexity of geographic expressions in natural language presents significant hurdles for traditional extraction methods. |
| Approach: | They propose a system that leverages large language models to extract spatial information from natural language. |
| Outcome: | SpatialWebAgent is designed to extract, standardize, and ground spatial information from natural language text directly onto maps. |
Answering Complex Geographic Questions by Adaptive Reasoning with Visual Context and External Commonsense Knowledge (2025.acl-long)
Copied to clipboard
| Challenge: | a new task of answering geographic reasoning questions based on the given image is proposed . the task requires identifying the objects in the image and understanding the background context . |
| Approach: | They propose a task of answering geographic reasoning questions based on the given image . they analyze the image and describe its fine-grained content by text and keywords . |
| Outcome: | The proposed method can be used to answer geographic reasoning questions based on an image . it can be applied to a large-scale dataset with 41,329 samples . |
Tagging Location Phrases in Text (2020.lrec-1)
Copied to clipboard
| Challenge: | a number of studies have focused on detecting named entities in written language. |
| Approach: | They describe a Location Phrase Detection task to detect non-named locations . they use sequential tagging and an annotation approach to create annotated datasets . |
| Outcome: | The proposed task can detect non-named locations in English and Russian news . the authors develop a sequential tagging approach and annotate datasets for English and Russia . |