Location Reference Recognition from Texts: A Survey and Comparison

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs

Where are you talking about? Advances and Challenges of Geographic Analysis of Text with Application to Disease Monitoring

The Natural Language Processing task we focus on in this thesis is Geoparsing. Geoparsing is the process of extraction and grounding of toponyms (place names). Consider this sentence: "The victims of the Spanish earthquake off the coast of Malaga were of American and Mexican origin." Four toponyms will be extracted (called Geotagging) and grounded to their geographic coordinates (called Toponym Resolution). However, our research goes further than any previous work by showing how to distinguish the literal place(s) of the event (Spain, Malaga) from other linguistic types/uses such as nationalities (Mexican, American), improving downstream task accuracy. We consolidate and extend the Standard Evaluation Framework, discuss key research problems, then present concrete solutions in order to advance each stage of geoparsing. For geotagging, as well as training a SOTA neural Location-NER tagger, we simplify Metonymy Resolution with a novel minimalist feature extraction combined with an LSTM-based classifier, matching SOTA results. For toponym resolution, we deploy the latest deep learning methods to achieve SOTA performance by augmenting neural models with hitherto unused geographic features called Map Vectors. With each research project, we provide high-quality datasets and system prototypes, further building resources in this field. We then show how these geoparsing advances coupled with our proposed Intra-Document Analysis can be used to associate news articles with locations in order to monitor the spread of public health threats. To this end, we evaluate our research contributions with production data from a real-time downstream application to improve geolocation of news events for disease monitoring. The data was made available to us by the Joint Research Centre (JRC), which operates one such system called MediSys that processes incoming news articles in order to monitor threats to public health and make these available to a variety of governmental, business and non-profit organisations. We also discuss steps towards an end-to-end, automated news monitoring system and make actionable recommendations for future work. In summary, the thesis aims are twofold: (1) Generate original geoparsing research aimed at advancing each stage of the pipeline by addressing pertinent challenges with concrete solutions and actionable proposals. (2) Demonstrate how this research can be applied to news event monitoring to increase the efficacy of existing biosurveillance systems, e.g. European Commission’s MediSys.I was generously funded by DREAM CDT, which was funded by NERC of UKRI

A pragmatic guide to geoparsing evaluation

Abstract: Empirical methods in geoparsing have thus far lacked a standard evaluation framework describing the task, metrics and data used to compare 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) and beyond. To address these deficiencies, our manuscript introduces a new framework in three parts. (Part 1) Task Definition: clarified via corpus linguistic analysis proposing a fine-grained Pragmatic Taxonomy of Toponyms. (Part 2) Metrics: discussed and reviewed for a rigorous evaluation including recommendations for NER/Geoparsing practitioners. (Part 3) Evaluation data: shared via a new dataset called GeoWebNews to provide test/train examples and enable immediate use of our contributions. In addition to fine-grained Geotagging and Toponym Resolution (Geocoding), this dataset is also suitable for prototyping and evaluating machine learning NLP models

Knowledge-based and data-driven approaches for geographical information access

Geographical Information Access (GeoIA) can be defined as a way of retrieving information from textual collections that includes the automatic analysis and interpretation of the geographical constraints and terms present in queries and documents. This PhD thesis presents, describes and evaluates several heterogeneous approaches for the following three GeoIA tasks: Geographical Information Retrieval (GIR), Geographical Question Answering (GeoQA), and Textual Georeferencing (TG). The GIR task deals with user queries that search over documents (e.g. ¿vineyards in California?) and the GeoQA task treats questions that retrieve answers (e.g. ¿What is the capital of France?). On the other hand, TG is the task of associate one or more georeferences (such as polygons or coordinates in a geodetic reference system) to electronic documents. Current state-of-the-art AI algorithms are not yet fully understanding the semantic meaning and the geographical constraints and terms present in queries and document collections. This thesis attempts to improve the effectiveness results of GeoIA tasks by: 1) improving the detection, understanding, and use of a part of the geographical and the thematic content of queries and documents with Toponym Recognition, Toponym Disambiguation and Natural Language Processing (NLP) techniques, and 2) combining Geographical Knowledge-Based Heuristics based on common sense with Data-Driven IR algorithms. The main contributions of this thesis to the state-of-the-art of GeoIA tasks are: 1) The presentation of 10 novel approaches for GeoIA tasks: 3 approaches for GIR, 3 for GeoQA, and 4 for Textual Georeferencing (TG). 2) The evaluation of these novel approaches in these contexts: within official evaluation benchmarks, after evaluation benchmarks with the test collections, and with other specific datasets. Most of these algorithms have been evaluated in international evaluations and some of them achieved top-ranked state-of-the-art results, including top-performing results in GIR (GeoCLEF 2007) and TG (MediaEval 2014) benchmarks. 3) The experiments reported in this PhD thesis show that the approaches can combine effectively Geographical Knowledge and NLP with Data-Driven techniques to improve the efectiveness measures of the three Geographical Information Access tasks investigated. 4) TALPGeoIR: a novel GIR approach that combines Geographical Knowledge ReRanking (GeoKR), NLP and Relevance Feedback (RF) that achieved state-of-the-art results in official GeoCLEF benchmarks (Ferrés and Rodríguez, 2008; Mandl et al., 2008) and posterior experiments (Ferrés and Rodríguez, 2015a). This approach has been evaluated with the full GeoCLEF corpus (100 topics) and showed that GeoKR, NLP, and RF techniques evaluated separately or in combination improve the results in MAP and R-Precision effectiveness measures of the state-of-the-art IR algorithms TF-IDF, BM25 and InL2 and show statistical significance in most of the experiments. 5) GeoTALP-QA: a scope-based GeoQA approach for Spanish and English and its evaluation with a set of questions of the Spanish geography (Ferrés and Rodríguez, 2006). 6) Four state-of-the-art Textual Georeferencing approaches for informal and formal documents that achieved state-of-the-art results in evaluation benchmarks (Ferrés and Rodríguez, 2014) and posterior experiments (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b).L'Accés a la Informació Geogràfica (GeoAI) pot ser definit com una forma de recuperar informació de col·lecions textuals que inclou l'anàlisi automàtic i la interpretació dels termes i restriccions geogràfiques que apareixen en consultes i documents. Aquesta tesi doctoral presenta, descriu i avalua varies aproximacions heterogènies a les seguents tasques de GeoAI: Recuperació de la Informació Geogràfica (RIG), Cerca de la Resposta Geogràfica (GeoCR), i Georeferenciament Textual (GT). La tasca de RIG tracta amb consultes d'usuari que cerquen documents (e.g. ¿vinyes a California?) i la tasca GeoCR tracta de recuperar respostes concretes a preguntes (e.g. ¿Quina és la capital de França?). D'altra banda, GT es la tasca de relacionar una o més referències geogràfiques (com polígons o coordenades en un sistema de referència geodètic) a documents electrònics. Els algoritmes de l'estat de l'art actual en Intel·ligència Artificial encara no comprenen completament el significat semàntic i els termes i les restriccions geogràfiques presents en consultes i col·leccions de documents. Aquesta tesi intenta millorar els resultats en efectivitat de les tasques de GeoAI de la seguent manera: 1) millorant la detecció, comprensió, i la utilització d'una part del contingut geogràfic i temàtic de les consultes i documents amb tècniques de reconeixement de topònims, desambiguació de topònims, i Processament del Llenguatge Natural (PLN), i 2) combinant heurístics basats en Coneixement Geogràfic i en el sentit comú humà amb algoritmes de Recuperació de la Informació basats en dades. Les principals contribucions d'aquesta tesi a l'estat de l'art de les tasques de GeoAI són: 1) La presentació de 10 noves aproximacions a les tasques de GeoAI: 3 aproximacions per RIG, 3 per GeoCR, i 4 per Georeferenciament Textual (GT). 2) L'avaluació d'aquestes noves aproximacions en aquests contexts: en el marc d'avaluacions comparatives internacionals, posteriorment a avaluacions comparatives internacionals amb les col·lections de test, i amb altres conjunts de dades específics. La majoria d'aquests algoritmes han estat avaluats en avaluacions comparatives internacionals i alguns d'ells aconseguiren alguns dels millors resultats en l'estat de l'art, com per exemple els resultats en comparatives de RIG (GeoCLEF 2007) i GT (MediaEval 2014). 3) Els experiments descrits en aquesta tesi mostren que les aproximacions poden combinar coneixement geogràfic i PLN amb tècniques basades en dades per millorar les mesures d'efectivitat en les tres tasques de l'Accés a la Informació Geogràfica investigades. 4) TALPGeoIR: una nova aproximació a la RIG que combina Re-Ranking amb Coneixement Geogràfic (GeoKR), PLN i Retroalimentació de Rellevancia (RR) que aconseguí resultats en l'estat de l'art en comparatives oficials GeoCLEF (Ferrés and Rodríguez, 2008; Mandl et al., 2008) i en experiments posteriors (Ferrés and Rodríguez, 2015a). Aquesta aproximació ha estat avaluada amb el conjunt complert del corpus GeoCLEF (100 topics) i ha mostrat que les tècniques GeoKR, PLN i RR avaluades separadament o en combinació milloren els resultats en les mesures efectivitat MAP i R-Precision dels algoritmes de l'estat de l'art en Recuperació de la Infomació TF-IDF, BM25 i InL2 i a més mostren significació estadística en la majoria dels experiments. 5) GeoTALP-QA: una aproximació basada en l'àmbit geogràfic per espanyol i anglès i la seva avaluació amb un conjunt de preguntes de la geografía espanyola (Ferrés and Rodríguez, 2006). 6) Quatre aproximacions per al georeferenciament de documents formals i informals que obtingueren resultats en l'estat de l'art en avaluacions comparatives (Ferrés and Rodríguez, 2014) i en experiments posteriors (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b).Postprint (published version

Knowledge-based and data-driven approaches for geographical information access

Geographical Information Access (GeoIA) can be defined as a way of retrieving information from textual collections that includes the automatic analysis and interpretation of the geographical constraints and terms present in queries and documents. This PhD thesis presents, describes and evaluates several heterogeneous approaches for the following three GeoIA tasks: Geographical Information Retrieval (GIR), Geographical Question Answering (GeoQA), and Textual Georeferencing (TG). The GIR task deals with user queries that search over documents (e.g. ¿vineyards in California?) and the GeoQA task treats questions that retrieve answers (e.g. ¿What is the capital of France?). On the other hand, TG is the task of associate one or more georeferences (such as polygons or coordinates in a geodetic reference system) to electronic documents. Current state-of-the-art AI algorithms are not yet fully understanding the semantic meaning and the geographical constraints and terms present in queries and document collections. This thesis attempts to improve the effectiveness results of GeoIA tasks by: 1) improving the detection, understanding, and use of a part of the geographical and the thematic content of queries and documents with Toponym Recognition, Toponym Disambiguation and Natural Language Processing (NLP) techniques, and 2) combining Geographical Knowledge-Based Heuristics based on common sense with Data-Driven IR algorithms. The main contributions of this thesis to the state-of-the-art of GeoIA tasks are: 1) The presentation of 10 novel approaches for GeoIA tasks: 3 approaches for GIR, 3 for GeoQA, and 4 for Textual Georeferencing (TG). 2) The evaluation of these novel approaches in these contexts: within official evaluation benchmarks, after evaluation benchmarks with the test collections, and with other specific datasets. Most of these algorithms have been evaluated in international evaluations and some of them achieved top-ranked state-of-the-art results, including top-performing results in GIR (GeoCLEF 2007) and TG (MediaEval 2014) benchmarks. 3) The experiments reported in this PhD thesis show that the approaches can combine effectively Geographical Knowledge and NLP with Data-Driven techniques to improve the efectiveness measures of the three Geographical Information Access tasks investigated. 4) TALPGeoIR: a novel GIR approach that combines Geographical Knowledge ReRanking (GeoKR), NLP and Relevance Feedback (RF) that achieved state-of-the-art results in official GeoCLEF benchmarks (Ferrés and Rodríguez, 2008; Mandl et al., 2008) and posterior experiments (Ferrés and Rodríguez, 2015a). This approach has been evaluated with the full GeoCLEF corpus (100 topics) and showed that GeoKR, NLP, and RF techniques evaluated separately or in combination improve the results in MAP and R-Precision effectiveness measures of the state-of-the-art IR algorithms TF-IDF, BM25 and InL2 and show statistical significance in most of the experiments. 5) GeoTALP-QA: a scope-based GeoQA approach for Spanish and English and its evaluation with a set of questions of the Spanish geography (Ferrés and Rodríguez, 2006). 6) Four state-of-the-art Textual Georeferencing approaches for informal and formal documents that achieved state-of-the-art results in evaluation benchmarks (Ferrés and Rodríguez, 2014) and posterior experiments (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b).L'Accés a la Informació Geogràfica (GeoAI) pot ser definit com una forma de recuperar informació de col·lecions textuals que inclou l'anàlisi automàtic i la interpretació dels termes i restriccions geogràfiques que apareixen en consultes i documents. Aquesta tesi doctoral presenta, descriu i avalua varies aproximacions heterogènies a les seguents tasques de GeoAI: Recuperació de la Informació Geogràfica (RIG), Cerca de la Resposta Geogràfica (GeoCR), i Georeferenciament Textual (GT). La tasca de RIG tracta amb consultes d'usuari que cerquen documents (e.g. ¿vinyes a California?) i la tasca GeoCR tracta de recuperar respostes concretes a preguntes (e.g. ¿Quina és la capital de França?). D'altra banda, GT es la tasca de relacionar una o més referències geogràfiques (com polígons o coordenades en un sistema de referència geodètic) a documents electrònics. Els algoritmes de l'estat de l'art actual en Intel·ligència Artificial encara no comprenen completament el significat semàntic i els termes i les restriccions geogràfiques presents en consultes i col·leccions de documents. Aquesta tesi intenta millorar els resultats en efectivitat de les tasques de GeoAI de la seguent manera: 1) millorant la detecció, comprensió, i la utilització d'una part del contingut geogràfic i temàtic de les consultes i documents amb tècniques de reconeixement de topònims, desambiguació de topònims, i Processament del Llenguatge Natural (PLN), i 2) combinant heurístics basats en Coneixement Geogràfic i en el sentit comú humà amb algoritmes de Recuperació de la Informació basats en dades. Les principals contribucions d'aquesta tesi a l'estat de l'art de les tasques de GeoAI són: 1) La presentació de 10 noves aproximacions a les tasques de GeoAI: 3 aproximacions per RIG, 3 per GeoCR, i 4 per Georeferenciament Textual (GT). 2) L'avaluació d'aquestes noves aproximacions en aquests contexts: en el marc d'avaluacions comparatives internacionals, posteriorment a avaluacions comparatives internacionals amb les col·lections de test, i amb altres conjunts de dades específics. La majoria d'aquests algoritmes han estat avaluats en avaluacions comparatives internacionals i alguns d'ells aconseguiren alguns dels millors resultats en l'estat de l'art, com per exemple els resultats en comparatives de RIG (GeoCLEF 2007) i GT (MediaEval 2014). 3) Els experiments descrits en aquesta tesi mostren que les aproximacions poden combinar coneixement geogràfic i PLN amb tècniques basades en dades per millorar les mesures d'efectivitat en les tres tasques de l'Accés a la Informació Geogràfica investigades. 4) TALPGeoIR: una nova aproximació a la RIG que combina Re-Ranking amb Coneixement Geogràfic (GeoKR), PLN i Retroalimentació de Rellevancia (RR) que aconseguí resultats en l'estat de l'art en comparatives oficials GeoCLEF (Ferrés and Rodríguez, 2008; Mandl et al., 2008) i en experiments posteriors (Ferrés and Rodríguez, 2015a). Aquesta aproximació ha estat avaluada amb el conjunt complert del corpus GeoCLEF (100 topics) i ha mostrat que les tècniques GeoKR, PLN i RR avaluades separadament o en combinació milloren els resultats en les mesures efectivitat MAP i R-Precision dels algoritmes de l'estat de l'art en Recuperació de la Infomació TF-IDF, BM25 i InL2 i a més mostren significació estadística en la majoria dels experiments. 5) GeoTALP-QA: una aproximació basada en l'àmbit geogràfic per espanyol i anglès i la seva avaluació amb un conjunt de preguntes de la geografía espanyola (Ferrés and Rodríguez, 2006). 6) Quatre aproximacions per al georeferenciament de documents formals i informals que obtingueren resultats en l'estat de l'art en avaluacions comparatives (Ferrés and Rodríguez, 2014) i en experiments posteriors (Ferrés and Rodríguez, 2011; Ferrés and Rodríguez, 2015b)

Location reference recognition from texts: A survey and comparison

A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to the process of recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of the specific applications is still missing. Further, there lacks a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and a core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching-based, statistical learning-based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references across the world. Results from this thorough evaluation can help inform future methodological developments for location reference recognition, and can help guide the selection of proper approaches based on application needs

Automatic reconstruction of itineraries from descriptive texts

Esta tesis se inscribe dentro del marco del proyecto PERDIDO donde los objetivos son la extracción y reconstrucción de itinerarios a partir de documentos textuales. Este trabajo se ha realizado en colaboración entre el laboratorio LIUPPA de l' Université de Pau et des Pays de l' Adour (France), el grupo de Sistemas de Información Avanzados (IAAA) de la Universidad de Zaragoza y el laboratorio COGIT de l' IGN (France). El objetivo de esta tesis es concebir un sistema automático que permita extraer, a partir de guías de viaje o descripciones de itinerarios, los desplazamientos, además de representarlos sobre un mapa. Se propone una aproximación para la representación automática de itinerarios descritos en lenguaje natural. Nuestra propuesta se divide en dos tareas principales. La primera pretende identificar y extraer de los textos describiendo itinerarios información como entidades espaciales y expresiones de desplazamiento o percepción. El objetivo de la segunda tarea es la reconstrucción del itinerario. Nuestra propuesta combina información local extraída gracias al procesamiento del lenguaje natural con datos extraídos de fuentes geográficas externas (por ejemplo, gazetteers). La etapa de anotación de informaciones espaciales se realiza mediante una aproximación que combina el etiquetado morfo-sintáctico y los patrones léxico-sintácticos (cascada de transductores) con el fin de anotar entidades nombradas espaciales y expresiones de desplazamiento y percepción. Una primera contribución a la primera tarea es la desambiguación de topónimos, que es un problema todavía mal resuelto dentro del reconocimiento de entidades nombradas (Named Entity Recognition - NER) y esencial en la recuperación de información geográfica. Se plantea un algoritmo no supervisado de georreferenciación basado en una técnica de clustering capaz de proponer una solución para desambiguar los topónimos los topónimos encontrados en recursos geográficos externos, y al mismo tiempo, la localización de topónimos no referenciados. Se propone un modelo de grafo genérico para la reconstrucción automática de itinerarios, donde cada nodo representa un lugar y cada arista representa un camino enlazando dos lugares. La originalidad de nuestro modelo es que además de tener en cuenta los elementos habituales (caminos y puntos del recorrido), permite representar otros elementos involucrados en la descripción de un itinerario, como por ejemplo los puntos de referencia visual. Se calcula de un árbol de recubrimiento mínimo a partir de un grafo ponderado para obtener automáticamente un itinerario bajo la forma de un grafo. Cada arista del grafo inicial se pondera mediante un método de análisis multicriterio que combina criterios cualitativos y cuantitativos. El valor de estos criterios se determina a partir de informaciones extraídas del texto e informaciones provenientes de recursos geográficos externos. Por ejemplo, se combinan las informaciones generadas por el procesamiento del lenguaje natural como las relaciones espaciales describiendo una orientación (ej: dirigirse hacia el sur) con las coordenadas geográficas de lugares encontrados dentro de los recursos para determinar el valor del criterio ``relación espacial''. Además, a partir de la definición del concepto de itinerario y de las informaciones utilizadas en la lengua para describir un itinerario, se ha modelado un lenguaje de anotación de información espacial adaptado a la descripción de desplazamientos, apoyándonos en las recomendaciones del consorcio TEI (Text Encoding and Interchange). Finalmente, se ha implementado y evaluado las diferentes etapas de nuestra aproximación sobre un corpus multilingüe de descripciones de senderos y excursiones (francés, español, italiano)

A survey on the geographic scope of textual documents.

Recognizing references to places in texts is needed in many applications, such assearch engines,loca- tion-based social media and document classification. In this paper we present a survey of methods and techniques for there cognition and identification of places referenced in texts. We discuss concept sand terminology, and propose a classification of the solutions given in the literature. We introduce a definition of the Geographic Scope Resolution (GSR) problem, dividing it in three steps: geoparsing, reference resolution, and grounding references. Solutions to the first two steps are organized according to the method used, and solutions to the third step are organized according to the type of out put produced. We found that it is difficult to compare existing solutions directly to one another, because they of ten create their own bench marking data, targeted to their own problem

Toponym Disambiguation in Information Retrieval

In recent years, geography has acquired a great importance in the context of Information Retrieval (IR) and, in general, of the automated processing of information in text. Mobile devices that are able to surf the web and at the same time inform about their position are now a common reality, together with applications that can exploit this data to provide users with locally customised information, such as directions or advertisements. Therefore, it is important to deal properly with the geographic information that is included in electronic texts. The majority of such kind of information is contained as place names, or toponyms. Toponym ambiguity represents an important issue in Geographical Information Retrieval (GIR), due to the fact that queries are geographically constrained. There has been a struggle to nd speci c geographical IR methods that actually outperform traditional IR techniques. Toponym ambiguity may constitute a relevant factor in the inability of current GIR systems to take advantage from geographical knowledge. Recently, some Ph.D. theses have dealt with Toponym Disambiguation (TD) from di erent perspectives, from the development of resources for the evaluation of Toponym Disambiguation (Leidner (2007)) to the use of TD to improve geographical scope resolution (Andogah (2010)). The Ph.D. thesis presented here introduces a TD method based on WordNet and carries out a detailed study of the relationship of Toponym Disambiguation to some IR applications, such as GIR, Question Answering (QA) and Web retrieval. The work presented in this thesis starts with an introduction to the applications in which TD may result useful, together with an analysis of the ambiguity of toponyms in news collections. It could not be possible to study the ambiguity of toponyms without studying the resources that are used as placename repositories; these resources are the equivalent to language dictionaries, which provide the di erent meanings of a given word.Buscaldi, D. (2010). Toponym Disambiguation in Information Retrieval [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8912Palanci