Biomedical Information Extraction Pipelines for Public Health in the Age of Deep Learning

abstract: Unstructured texts containing biomedical information from sources such as electronic health records, scientific literature, discussion forums, and social media offer an opportunity to extract information for a wide range of applications in biomedical informatics. Building scalable and efficient pipelines for natural language processing and extraction of biomedical information plays an important role in the implementation and adoption of applications in areas such as public health. Advancements in machine learning and deep learning techniques have enabled rapid development of such pipelines. This dissertation presents entity extraction pipelines for two public health applications: virus phylogeography and pharmacovigilance. For virus phylogeography, geographical locations are extracted from biomedical scientific texts for metadata enrichment in the GenBank database containing 2.9 million virus nucleotide sequences. For pharmacovigilance, tools are developed to extract adverse drug reactions from social media posts to open avenues for post-market drug surveillance from non-traditional sources. Across these pipelines, high variance is observed in extraction performance among the entities of interest while using state-of-the-art neural network architectures. To explain the variation, linguistic measures are proposed to serve as indicators for entity extraction performance and to provide deeper insight into the domain complexity and the challenges associated with entity extraction. For both the phylogeography and pharmacovigilance pipelines presented in this work the annotated datasets and applications are open source and freely available to the public to foster further research in public health.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

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)

Geocoding location expressions in Twitter messages: A preference learning method

Resolving location expressions in text to the correct physical location, also known as geocoding or grounding, is complicated by the fact that so many places around the world share the same name. Correct resolution is made even more difficult when there is little context to determine which place is intended, as in a 140-character Twitter message, or when location cues from different sources conflict, as may be the case among different metadata fields of a Twitter message. We used supervised machine learning to weigh the different fields of the Twitter message and the features of a world gazetteer to create a model that will prefer the correct gazetteer candidate to resolve the extracted expression. We evaluated our model using the F1 measure and compared it to similar algorithms. Our method achieved results higher than state-of-the-art competitors

Semantic Enrichment of a Multilingual Archive with Linked Open Data

This paper introduces MERCKX, a Multilingual Entity/Resource Combiner & Knowledge eXtractor. A case study involving the semantic enrichment of a multilingual archive is presented with the aim of assessing the relevance of natural language processing techniques such as named-entity recognition and entity linking for cultural heritage material. In order to improve the indexing of historical collections, we map entities to the Linked Open Data cloud using a language-independent method. Our evaluation shows that MERCKX outperforms similar tools on the task of place disambiguation and linking, achieving over 80% precision despite lower recall scores. These results are encouraging for small and medium-size cultural institutions since they demonstrate that semantic enrichment can be achieved with limited resources.Peer reviewe

A Coherent Unsupervised Model for Toponym Resolution

Toponym Resolution, the task of assigning a location mention in a document to a geographic referent (i.e., latitude/longitude), plays a pivotal role in analyzing location-aware content. However, the ambiguities of natural language and a huge number of possible interpretations for toponyms constitute insurmountable hurdles for this task. In this paper, we study the problem of toponym resolution with no additional information other than a gazetteer and no training data. We demonstrate that a dearth of large enough annotated data makes supervised methods less capable of generalizing. Our proposed method estimates the geographic scope of documents and leverages the connections between nearby place names as evidence to resolve toponyms. We explore the interactions between multiple interpretations of mentions and the relationships between different toponyms in a document to build a model that finds the most coherent resolution. Our model is evaluated on three news corpora, two from the literature and one collected and annotated by us; then, we compare our methods to the state-of-the-art unsupervised and supervised techniques. We also examine three commercial products including Reuters OpenCalais, Yahoo! YQL Placemaker, and Google Cloud Natural Language API. The evaluation shows that our method outperforms the unsupervised technique as well as Reuters OpenCalais and Google Cloud Natural Language API on all three corpora; also, our method shows a performance close to that of the state-of-the-art supervised method and outperforms it when the test data has 40% or more toponyms that are not seen in the training data.Comment: 9 pages (+1 page reference), WWW '18 Proceedings of the 2018 World Wide Web Conferenc

Ontology-driven urban issues identification from social media.

As cidades em todo o mundo enfrentam muitos problemas diretamente relacionados ao espaço urbano, especialmente nos aspectos de infraestrutura. A maioria desses problemas urbanos geralmente afeta a vida de residentes e visitantes. Por exemplo, as pessoas podem relatar um carro estacionado em uma calçada que está forçando os pedestres a andar na via, ou um enorme buraco que está causando congestionamento. Além de estarem relacionados com o espaço urbano, os problemas urbanos geralmente demandam ações das autoridades municipais. Existem diversas Redes Sociais Baseadas em Localização (LBSN, em inglês) no domínio das cidades inteligentes em todo o mundo, onde as pessoas relatam problemas urbanos de forma estruturada e as autoridades locais tomam conhecimento para então solucioná-los. Com o advento das redes sociais como Facebook e Twitter, as pessoas tendem a reclamar de forma não estruturada, esparsa e imprevisível, sendo difícil identificar problemas urbanos eventualmente relatados. Dados de mídia social, especialmente mensagens do Twitter, fotos e check-ins, tem desempenhado um papel importante nas cidades inteligentes. Um problema chave é o desafio de identificar conversas específicas e relevantes ao processar dados crowdsourcing ruidosos. Neste contexto, esta pesquisa investiga métodos computacionais a fim de fornecer uma identificação automatizada de problemas urbanos compartilhados em mídias sociais. A maioria dos trabalhos relacionados depende de classificadores baseados em técnicas de aprendizado de máquina, como SVM, Naïve Bayes e Árvores de Decisão; e enfrentam problemas relacionados à representação do conhecimento semântico, legibilidade humana e capacidade de inferência. Com o objetivo de superar essa lacuna semântica, esta pesquisa investiga a Extração de Informação baseada em ontologias, a partir da perspectiva de problemas urbanos, uma vez que tais problemas podem ser semanticamente interligados em plataformas LBSN. Dessa forma, este trabalho propõe uma ontologia no domínio de Problemas Urbanos (UIDO) para viabilizar a identificação e classificação dos problemas urbanos em uma abordagem automatizada que foca principalmente nas facetas temática e geográfica. Uma avaliação experimental demonstra que o desempenho da abordagem proposta é competitivo com os algoritmos de aprendizado de máquina mais utilizados, quando aplicados a este domínio em particular.The cities worldwide face with many issues directly related to the urban space, especially in the infrastructure aspects. Most of these urban issues generally affect the life of both resident and visitant people. For example, people can report a car parked on a footpath which is forcing pedestrians to walk on the road or a huge pothole that is causing traffic congestion. Besides being related to the urban space, urban issues generally demand actions from city authorities. There are many Location-Based Social Networks (LBSN) in the smart cities domain worldwide where people complain about urban issues in a structured way and local authorities are aware to fix them. With the advent of social networks such as Facebook and Twitter, people tend to complain in an unstructured, sparse and unpredictable way, being difficult to identify urban issues eventually reported. Social media data, especially Twitter messages, photos, and check-ins, have played an important role in the smart cities. A key problem is the challenge in identifying specific and relevant conversations on processing the noisy crowdsourced data. In this context, this research investigates computational methods in order to provide automated identification of urban issues shared in social media streams. Most related work rely on classifiers based on machine learning techniques such as Support Vector Machines (SVM), Naïve Bayes and Decision Trees; and face problems concerning semantic knowledge representation, human readability and inference capability. Aiming at overcoming this semantic gap, this research investigates the ontology-driven Information Extraction (IE) from the perspective of urban issues; as such issues can be semantically linked in LBSN platforms. Therefore, this work proposes an Urban Issues Domain Ontology (UIDO) to enable the identification and classification of urban issues in an automated approach that focuses mainly on the thematic and geographical facets. Experimental evaluation demonstrates the proposed approach performance is competitive with most commonly used machine learning algorithms applied for that particular domain.CNP

An open-source framework for publishing geographical names – A case study of Kenya

Az önkéntes földrajzi adatok (volunteered geographic information, VGI) mára egy jelentős erőforrássá váltak, amelyek szabadon elérhetők a világhálón. Közösségi alapú projektként óriási az alkalmazási potenciáljuk a földrajzinév-tárak fejlesztésében, alkalmazásukkal kis költséggel lehet akár a hivatalos nemzeti térképműveket is naprakészen tartani. Jelen cikk célja egy nyílt forráskódú keretrendszer segítségével fejlesztett digitális földrajzinévtárszolgáltatás bemutatása, amelyet egy webes és mobilalkalmazás támogat. A szerzők létrehoztak egy, a Django keretrendszeren alapuló szolgáltatást, mely egy PostgreSQL adatbázisban tárolja az összegyűjtött adatokat. A webes és a mobilalkalmazás összekapcsolása az adatbázissal, a gyűjtött adatok feltöltése egy REST végponton keresztül valósul meg. A földrajzinévtár-szolgáltatás különféle térképi vizualizációs eszközöket kínál a helynevek és a köztük lévő kapcsolatok bemutatására a szöveges korpusz alapján. A szolgáltatás testre szabható bármely földrajzi területen történő használatra a konfiguráció megfelelő beállításával, és az engedélyezett adatmennyiség és a szolgáltatási hitelesítők számkorlátozásának feloldásával, beleértve a funkcionalitás növelését. A névtár-keretrendszer tesztpéldányát az elérhető kenyai földrajzi nevekkel feltöltve és tesztelve az eredmények és a rendszer teljesítménye átlagon felüli eredményeket mutatnak a névtár-bejegyzések frissítése, az információk térképi megjelenítése és földrajzinév-örökség megőrzésének területén. A Django API keretrendszer összes kódfájlja és kódkonfigurációja elérhető az Atlassian online tárhelyen

Geospatial Analysis and Modeling of Textual Descriptions of Pre-modern Geography

Textual descriptions of pre-modern geography offer a different view of classical geography. The descriptions have been produced when none of the modern geographical concepts and tools were available. In this dissertation, we study pre-modern geography by primarily finding the existing structures of the descriptions and different cases of geographical data. We first explain four major geographical cases in pre-modern Arabic sources: gazetteer, administrative hierarchies, routes, and toponyms associated with people. Focusing on hierarchical divisions and routes, we offer approaches for manual annotation of administrative hierarchies and route sections as well as a semi-automated toponyms annotation. The latter starts with a fuzzy search of toponyms from an authority list and applies two different extrapolation models to infer true or false values, based on the context, for disambiguating the automatically annotated toponyms. Having the annotated data, we introduce mathematical models to shape and visualize regions based on the description of administrative hierarchies. Moreover, we offer models for comparing hierarchical divisions and route networks from different sources. We also suggest approaches to approximate geographical coordinates for places that do not have geographical coordinates - we call them unknown places - which is a major issue in visualization of pre-modern places on map. The final chapter of the dissertation introduces the new version of al-Ṯurayyā, a gazetteer and a spatial model of the classical Islamic world using georeferenced data of a pre-modern atlas with more than 2, 000 toponyms and routes. It offers search, path finding, and flood network functionalities as well as visualizations of regions using one of the models that we describe for regions. However the gazetteer is designed using the classical Islamic world data, the spatial model and features can be used for similarly prepared datasets.:1 Introduction 1 2 Related Work 8 2.1 GIS 8 2.2 NLP, Georeferencing, Geoparsing, Annotation 10 2.3 Gazetteer 15 2.4 Modeling 17 3 Classical Geographical Cases 20 3.1 Gazetteer 21 3.2 Routes and Travelogues 22 3.3 Administrative Hierarchy 24 3.4 Geographical Aspects of Biographical Data 25 4 Annotation and Extraction 27 4.1 Annotation 29 4.1.1 Manual Annotation of Geographical Texts 29 4.1.1.1 Administrative Hierarchy 30 4.1.1.2 Routes and Travelogues 32 4.1.2 Semi-Automatic Toponym Annotation 34 4.1.2.1 The Annotation Process 35 4.1.2.2 Extrapolation Models 37 4.1.2.2.1 Frequency of Toponymic N-grams 37 4.1.2.2.2 Co-occurrence Frequencies 38 4.1.2.2.3 A Supervised ML Approach 40 4.1.2.3 Summary 45 4.2 Data Extraction and Structures 45 4.2.1 Administrative Hierarchy 45 4.2.2 Routes and Distances 49 5 Modeling Geographical Data 51 5.1 Mathematical Models for Administrative Hierarchies 52 5.1.1 Sample Data 53 5.1.2 Quadtree 56 5.1.3 Voronoi Diagram 58 5.1.4 Voronoi Clippings 62 5.1.4.1 Convex Hull 62 5.1.4.2 Concave Hull 63 5.1.5 Convex Hulls 65 5.1.6 Concave Hulls 67 5.1.7 Route Network 69 5.1.8 Summary of Models for Administrative Hierarchy 69 5.2 Comparison Models 71 5.2.1 Hierarchical Data 71 5.2.1.1 Test Data 73 5.2.2 Route Networks 76 5.2.2.1 Post-processing 81 5.2.2.2 Applications 82 5.3 Unknown Places 84 6 Al-Ṯurayyā 89 6.1 Introducing al-Ṯurayyā 90 6.2 Gazetteer 90 6.3 Spatial Model 91 6.3.1 Provinces and Administrative Divisions 93 6.3.2 Pathfinding and Itineraries 93 6.3.3 Flood Network 96 6.3.4 Path Alignment Tool 97 6.3.5 Data Structure 99 6.3.5.1 Places 100 6.3.5.2 Routes and Distances 100 7 Conclusions and Further Work 10