Geoparsing

Empirical methods in geoparsing have thus far lacked a standard evaluation framework describing the task, data and metrics used to establish state-of-the-art systems. Evaluation is further made inconsistent, even unrepresentative of real world usage, by the lack of distinction between the different types of toponyms, which necessitates new guidelines, a consolidation of metrics and a detailed toponym taxonomy with implications for Named Entity Recognition (NER). To address these deficiencies, our manuscript introduces such a framework in three parts. Part 1) Task Definition: clarified via corpus linguistic analysis proposing a fine-grained Pragmatic Taxonomy of Toponyms with new guidelines. Part 2) Evaluation Data: shared via a dataset called GeoWebNews to provide test/train data to enable immediate use of our contributions. In addition to fine-grained Geotagging and Toponym Resolution (Geocoding), this dataset is also suitable for prototyping machine learning NLP models. Part 3) Metrics: discussed and reviewed for a rigorous evaluation with appropriate recommendations for NER/Geoparsing practitioners.

The purpose of text geolocation is to associate geographic information contained in a document with a set (or sets) of coordinates , either implicitly by using linguistic features and/or explicitly by using geographic metadata combined with heuristics. We introduce a geocoder (location mention disambiguator) that achieves state-of-the-art (SOTA) results on three diverse datasets by exploiting the implicit lexical clues. Moreover, we propose a new method for systematic encoding of geographic metadata to generate two distinct views of the same text. To that end, we introduce the Map Vector (MapVec), a sparse representation obtained by plotting prior geographic probabilities, derived from population figures, on a World Map. We then integrate the implicit (language) and explicit (map) features to significantly improve a range of metrics. We also introduce an open-source dataset for geoparsing of news events covering global disease outbreaks and epidemics to help future evaluation in geoparsing.

Geographical data can be obtained by converting place names from free-format text into geographical coordinates. The ability to geo-locate events in textual reports represents a valuable source of information in many real-world applications such as emergency responses, real-time social media geographical event analysis, understanding location instructions in auto-response systems and more. However, geoparsing is still widely regarded as a challenge because of domain language diversity, place name ambiguity, metonymic language and limited leveraging of context as we show in our analysis. Results to date, whilst promising, are on laboratory data and unlike in wider NLP are often not cross-compared. In this study, we evaluate and analyse the performance of a number of leading geoparsers on a number of corpora and highlight the challenges in detail. We also publish an automatically geotagged Wikipedia corpus to alleviate the dearth of (open source) corpora in this domain.

Fyysinen aktiivisuus vaikuttaa vahvasti yksilön terveyteen ja hyvinvointiin. Alueellisen eriytymisen ehkäisyn ja ympäristöllisen tasa-arvon kannalta on tärkeää, että eri alueiden asukkailla on yhtäläiset mahdollisuudet harrastaa liikuntaa. Avoimesti saatavilla olevia kattavia tutkimuksia ihmisten fyysisestä aktiivisuudesta eri puolilla pääkaupunkiseutua ei juurikaan ole tehty, paikallisia liikuntabarometrejä lukuun ottamatta. Virallisten ja kattavien tietolähteiden puutteessa käyttäjien itse tuottamaa dataa, kuten sosiaalisen median dataa, voidaan mahdollisesti käyttää fyysisen aktiivisuuden arviointiin. Tässä tutkielmassa pyrin vastaamaan kysymyksiin: 1) kuinka Twitter-dataa voidaan käyttää indikaattorina liikunnallisen aktiivisuuden arviointiin, 2) miten liikunta-aiheistet twiitit ovat jakautuneet pääkaupunkiseudulla ja 3) mitkä sosio-ekonomiset tekijät selittävät twiittien lukumäärää alueella. Liikunta-aiheisten twiittien keräämiseen hyödynsin hakua urheiluun ja liikuntaan liitty...

Historical itineraries, often accessible as lists or tables describing places visited in sequence, are abundant resources and also important objects of study for humanities scholars. This article advances a novel method for automatically geocoding tabular itineraries, combining approximate string matching with a cost optimization algorithm based on dynamic programming. Experiments with a dataset of historical itineraries, with ground-truth geocoding annotations provided by domain experts and leveraging also the GeoN-ames gazetteer, attest to the effectiveness of the proposed method. The obtained results show that while approximate string matching can already achieve very low median errors, with many toponyms matching exactly against GeoNames entries, the combination with cost optimization can significantly improve results in terms of the average distance towards the correct disambiguations.

The automatic extraction of geospatial information in real time, from free speech, will an important impact on disruptive technologies, such as AI assistants and search engines. Models capable of decoding discourse and then transforming it into structured data addresses the solution of challenging problems such as: the auto- matic identification of phrases refering geospatial entities; the handling of synonyms referring to the same location (ambiguity); the taxonomic characterization of loca- tive expressions; as well as the automation of complex processes of natural language interpretation to determine of geographical coordinates, maps and databases. The presented project aims to automate georeferencing processes of digital documents to extract georeferenced knowledge. The methodological proposal includes compilation of a corpus, linguistic characterization and algorithms.
Keywords: Geoparsing, Geocoding, Named Entity Recognition