Affiliation:
1. University of Edinburgh, Edinburgh, Scotland, UK
Abstract
In Information Extraction (IE), processing of named entities in text has traditionally been seen as a two-step process comprising a flat text span recognition sub-task and an atomic classification sub-task; relating the text span to a model of the world has been ignored by evaluations such as DARPA/NIST's MUC or ACE. However, spatial and temporal expressions refer to events in space-time, and the grounding of events is a precondition for accurate reasoning. Thus, automatic grounding can improve many applications such as automatic map drawing (e.g. for choosing a focus) and question answering (e.g., for questions like
How far is London from Edinburgh
, given a story in which both occur and can be resolved). Whereas temporal grounding has received considerable attention in the recent Past [2, 3], robust spatial grounding has long been neglected. Concentrating on geographic names for populated places, I define the task of automatic
Toponym Resolution
(TR) as computing the mapping from occurrences of names for places as found in a text to a representation of the extensional semantics of the location referred to (its referent), such as a geographic latitude/longitude footprint. The task of mapping from names to locations is hard due to insufficient and noisy databases, and a large degree of ambiguity: common words need to be distinguished from proper names (geo/non-geo ambiguity), and the mapping between names and locations is ambiguous
London
can refer to the capital of the UK or to London, Ontario, Canada, or to about forty other Londons on earth). In addition, names of places and the boundaries referred to change over time, and databases are incomplete.
Publisher
Association for Computing Machinery (ACM)
Subject
Hardware and Architecture,Management Information Systems
Cited by
80 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. A Hierarchy-Aware Geocoding Model Based on Cross-Attention within the Seq2Seq Framework;ISPRS International Journal of Geo-Information;2024-04-17
2. MAWI: Mapping the Unmapped in Wikipedia via Geographic Information Extraction;Communications in Computer and Information Science;2024
3. TAME II: A Modern Geographic Text Annotation Tool;Lecture Notes in Computer Science;2024
4. The REThinkWASTE data integration and analytics platform for intelligent waste management;Proceedings of the IEEE/ACM 10th International Conference on Big Data Computing, Applications and Technologies;2023-12-04
5. Enriching Wikipedia Texts through Geographic Information Extraction;Proceedings of the International Conference on Advances in Social Networks Analysis and Mining;2023-11-06