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

How can voting mechanisms improve the robustness of individual toponym resolution approaches?

This paper investigates the feasibility of implementing a general and robust toponym resolution approach by ensembling multiple existing approaches through voting mechanisms. Experiments were conducted to compare two voting ensembles with nine individual approaches based on seven public datasets. The results show that the voting ensembles can achieve consistent measures of Accuracy@161km and mean error, outperforming the individual approaches

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

Extending the YAGO Knowledge Graph with Geospatial Knowledge

Η βάση γνώσης YAGO είναι μία από τις μεγαλύτερες βάσεις γνώσεις, που διαθέτουν τα δε- δομένα τους ως ανοιχτά διασυνδεδεμένα δεδομένα. Χωρική πληροφορία, δηλαδή η ανα- παράσταση της τοποθεσίας οντοτήτων με ένα σημείο, προστέθηκε στη δεύτερη έκδοση του YAGO. Σε αυτή τη δουλειά έχουμε ως σκοπό να επεκτείνουμε το γράφο γνώσης του YAGO με ποιοτική γεωχωρική πληροφορία (πολύγωνα και ευθείες), η οποία προέρχεται από πολλαπλές πηγές. Μελετήσαμε δεδομένα τα οποία διανέμονται όχι μόνο από έργα που βασίζονται στον πληθοπορισμό αλλά και από επίσημες πηγές διαφόρων κρατών. Εί- ναι σημαντικό να μην προσθέσουμε στο γράφο γνώσης πληροφορία που ήδη υπάρχει σε αυτόν και γι’ αυτό το λόγο ψάχνουμε συσχετίσεις μεταξύ των οντοτήτων του YAGO και εκείνων που ανήκουν στα σύνολα δεδομένων που εξετάσαμε. Τα αποτελέσματα δείχνουν πως η μεθοδολογία μας παρήγαγε συσχετίσεις με πολύ μεγάλη ακρίβεια. Στο τέλος της εργασίας αυτής παρουσιάζουμε τον επεκταμένο γράφο γνώσης.YAGO is one of the largest knowledge bases that provide their data as Linked Open Data. Spatial information, in the form of points, was introduced in YAGO2, the second version of YAGO. In this work we present an extension of YAGO with qualitative geospatial inform- ation (i.e., polygons and lines), which was extracted from multiple sources. We studied datasets that are provided from crowdsourced projects as well as from official sources of several countries. It is important to point out that we do not introduce duplicate information in the knowledge graph of YAGO, by creating entities that already exist. Hence, at first, we try to match entities of YAGO with the entities of the data sources that we used. Our results show that our methodology produced matches with very high precision. This work is concluded with a demonstration of the extended knowledge graph

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

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

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

Context-sensitive interpretation of natural language location descriptions : a thesis submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy in Information Technology at Massey University, Auckland, New Zealand

People frequently describe the locations of objects using natural language. Location descriptions may be either structured, such as 26 Victoria Street, Auckland, or unstructured. Relative location descriptions (e.g., building near Sky Tower) are a common form of unstructured location description, and use qualitative terms to describe the location of one object relative to another (e.g., near, close to, in, next to). Understanding the meaning of these terms is easy for humans, but much more difficult for machines since the terms are inherently vague and context sensitive. In this thesis, we study the semantics (or meaning) of qualitative, geospatial relation terms, specifically geospatial prepositions. Prepositions are one of the most common forms of geospatial relation term, and they are commonly used to describe the location of objects in the geographic (geospatial) environment, such as rivers, mountains, buildings, and towns. A thorough understanding of the semantics of geospatial relation terms is important because it enables more accurate automated georeferencing of text location descriptions than use of place names only. Location descriptions that use geospatial prepositions are found in social media, web sites, blogs, and academic reports, and georeferencing can allow mapping of health, disaster and biological data that is currently inaccessible to the public. Such descriptions have unstructured format, so, their analysis is not straightforward. The specific research questions that we address are: RQ1. Which geospatial prepositions (or groups of prepositions) and senses are semantically similar? RQ2. Is the role of context important in the interpretation of location descriptions? RQ3. Is the object distance associated with geospatial prepositions across a range of geospatial scenes and scales accurately predictable using machine learning methods? RQ4. Is human annotation a reliable form of annotation for the analysis of location descriptions? To address RQ1, we determine the nature and degree of similarity among geospatial prepositions by analysing data collected with a human subjects experiment, using clustering, extensional mapping and t-stochastic neighbour embedding (t-SNE) plots to form a semantic similarity matrix. In addition to calculating similarity scores among prepositions, we identify the senses of three groups of geospatial prepositions using Venn diagrams, t-sne plots and density-based clustering, and define the relationships between the senses. Furthermore, we use two text mining approaches to identify the degree of similarity among geospatial prepositions: bag of words and GloVe embeddings. By using these methods and further analysis, we identify semantically similar groups of geospatial prepositions including: 1- beside, close to, near, next to, outside and adjacent to; 2- across, over and through and 3- beyond, past, by and off. The prepositions within these groups also share senses. Through is recognised as a specialisation of both across and over. Proximity and adjacency prepositions also have similar senses that express orientation and overlapping relations. Past, off and by share a proximal sense but beyond has a different sense from these, representing on the other side. Another finding is the more frequent use of the preposition close to for pairs of linear objects than near, which is used more frequently for non-linear ones. Also, next to is used to describe proximity more than touching (in contrast to other prepositions like adjacent to). Our application of text mining to identify semantically similar prepositions confirms that a geospatial corpus (NCGL) provides a better representation of the semantics of geospatial prepositions than a general corpus. Also, we found that GloVe embeddings provide adequate semantic similarity measures for more specialised geospatial prepositions, but less so for those that have more generalised applications and multiple senses. We explore the role of context (RQ2) by studying three sites that vary in size, nature, and context in London: Trafalgar Square, Buckingham Palace, and Hyde Park. We use the Google search engine to extract location descriptions that contain these three sites with 9 different geospatial prepositions (in, on, at, next to, close to, adjacent to, near, beside, outside) and calculate their acceptance profiles (the profile of the use of a preposition at different distances from the reference object) and acceptance thresholds (maximum distance from a reference object at which a preposition can acceptably be used). We use these to compare prepositions, and to explore the influence of different contexts. Our results show that near, in and outside are used for larger distances, while beside, adjacent to and at are used for smaller distances. Also, the acceptance threshold for close to is higher than for other proximity/adjacency prepositions such as next to, adjacent to and beside. The acceptance threshold of next to is larger than adjacent to, which confirms the findings in ‎Chapter 2 which identifies next to describing a proximity rather than touching spatial relation. We also found that relatum characteristics such as image schema affect the use of prepositions such as in, on and at. We address RQ3 by developing a machine learning regression model (using the SMOReg algorithm) to predict the distance associated with use of geospatial prepositions in specific expressions. We incorporate a wide range of input variables including the similarity matrix of geospatial prepositions (RQ1); preposition senses; semantic information in the form of embeddings; characteristics of the located and reference objects in the expression including their liquidity/solidity, scale and geometry type and contextual factors such as the density of features of different types in the surrounding area. We evaluate the model on two different datasets with 25% improvement against the best baseline respectively. Finally, we consider the importance of annotation of geospatial location descriptions (RQ4). As annotated data is essential for the successful study of automated interpretation of natural language descriptions, we study the impact and accuracy of human annotation on different geospatial elements. Agreement scores show that human annotators can annotate geospatial relation terms (e.g., geospatial prepositions) with higher agreement than other geospatial elements. This thesis advances understanding of the semantics of geospatial prepositions, particularly considering their semantic similarity and the impact of context on their interpretation. We quantify the semantic similarity of a set of 24 geospatial prepositions; identify senses and the relationships among them for 13 geospatial prepositions; compare the acceptance thresholds of 9 geospatial prepositions and describe the influence of context on them; and demonstrate that richer semantic and contextual information can be incorporated in predictive models to interpret relative geospatial location descriptions more accurately