@phdthesis{leidner2007toponym, abstract = {Background. In the area of Geographic Information Systems (GIS), a shared discipline between informatics and geography, the term geo-parsing is used to describe the process of identifying names in text, which in computational linguistics is known as named entity recognition and classification (NERC). The term geo-coding is used for the task of mapping from implicitly geo-referenced datasets (such as structured address records) to explicitly geo-referenced representations (e.g., using latitude and longitude). However, present-day GIS systems provide no automatic geo-coding functionality for unstructured text. 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 MUC or ACE (Chinchor (1998); U.S. NIST (2003)). 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 (Mani and Wilson (2000); Setzer (2001)), 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. Objective. I investigate how referentially ambiguous spatial named entities can be grounded, or resolved, with respect to an extensional coordinate model robustly on open-domain news text. I begin by comparing the few algorithms proposed in the literature, and, comparing semiformal, reconstructed descriptions of them, I factor out a shared repertoire of linguistic heuristics (e.g. rules, patterns) and extra-linguistic knowledge sources (e.g. population sizes). I then investigate how to combine these sources of evidence to obtain a superior method. I also investigate the noise effect introduced by the named entity tagging step that toponym resolution relies on in a sequential system pipeline architecture. Scope. In this thesis, I investigate a present-day snapshot of terrestrial geography as represented in the gazetteer defined and, accordingly, a collection of present-day news text. I limit the investigation to populated places; geo-coding of artifact names (e.g. airports or bridges), compositional geographic descriptions (e.g. 40 miles SW of London, near Berlin), for instance, is not attempted. Historic change is a major factor affecting gazetteer construction and ultimately toponym resolution. However, this is beyond the scope of this thesis. Method. While a small number of previous attempts have been made to solve the toponym resolution problem, these were either not evaluated, or evaluation was done by manual inspection of system output instead of curating a reusable reference corpus. Since the relevant literature is scattered across several disciplines (GIS, digital libraries, information retrieval, natural language processing) and descriptions of algorithms are mostly given in informal prose, I attempt to systematically describe them and aim at a reconstruction in a uniform, semi-formal pseudo-code notation for easier re-implementation. A systematic comparison leads to an inventory of heuristics and other sources of evidence. In order to carry out a comparative evaluation procedure, an evaluation resource is required. Unfortunately, to date no gold standard has been curated in the research community. To this end, a reference gazetteer and an associated novel reference corpus with human-labeled referent annotation are created. These are subsequently used to benchmark a selection of the reconstructed algorithms and a novel re-combination of the heuristics catalogued in the inventory. I then compare the performance of the same TR algorithms under three different conditions, namely applying it to the (i) output of human named entity annotation, (ii) automatic annotation using an existing Maximum Entropy sequence tagging model, and (iii) a na ̈ve toponym lookup procedure in a gazetteer. Evaluation. The algorithms implemented in this thesis are evaluated in an intrinsic or component evaluation. To this end, we define a task-specific matching criterion to be used with traditional Precision (P) and Recall (R) evaluation metrics. This matching criterion is lenient with respect to numerical gazetteer imprecision in situations where one toponym instance is marked up with different gazetteer entries in the gold standard and the test set, respectively, but where these refer to the same candidate referent, caused by multiple near-duplicate entries in the reference gazetteer. Main Contributions. The major contributions of this thesis are as follows: • A new reference corpus in which instances of location named entities have been manually annotated with spatial grounding information for populated places, and an associated reference gazetteer, from which the assigned candidate referents are chosen. This reference gazetteer provides numerical latitude/longitude coordinates (such as 51◦ 32 North, 0◦ 5 West) as well as hierarchical path descriptions (such as London > UK) with respect to a world wide-coverage, geographic taxonomy constructed by combining several large, but noisy gazetteers. This corpus contains news stories and comprises two sub-corpora, a subset of the REUTERS RCV1 news corpus used for the CoNLL shared task (Tjong Kim Sang and De Meulder (2003)), and a subset of the Fourth Message Understanding Contest (MUC-4; Chinchor (1995)), both available pre-annotated with gold-standard. This corpus will be made available as a reference evaluation resource; • a new method and implemented system to resolve toponyms that is capable of robustly processing unseen text (open-domain online newswire text) and grounding toponym instances in an extensional model using longitude and latitude coordinates and hierarchical path descriptions, using internal (textual) and external (gazetteer) evidence; • an empirical analysis of the relative utility of various heuristic biases and other sources of evidence with respect to the toponym resolution task when analysing free news genre text; • a comparison between a replicated method as described in the literature, which functions a baseline, and a novel algorithm based on minimality heuristics; and • several exemplary prototypical applications to show how the resulting toponym resolution methods can be used to create visual surrogates for news stories, a geographic exploration tool for news browsing, geographically-aware document retrieval and to answer spatial questions (How far...?) in an open-domain question answering system. These applications only have demonstrative character, as a thorough quantitative, task-based (extrinsic) evaluation of the utility of automatic toponym resolution is beyond the scope of this thesis and left for future work. }, author = {Leidner, Jochen Lothar}, interhash = {4558afaf4c48986f34b04bf06169456e}, intrahash = {c5f99e5f0fc60d29fcf730b968a95e90}, school = {School of Informatics, University of Edinburgh}, title = {Toponym Resolution in Text: Annotation, Evaluation and Applications of Spatial Grounding of Place Names}, url = {http://www.era.lib.ed.ac.uk/bitstream/1842/1849/1/leidner-2007-phd.pdf}, year = 2007 } @inproceedings{garbin2005disambiguating, abstract = {This research is aimed at the problem of disambiguating toponyms (place names) in terms of a classification derived by merging information from two publicly available gazetteers. To establish the difficulty of the problem, we measured the degree of ambiguity, with respect to a gazetteer, for toponyms in news. We found that 67.82% of the toponyms found in a corpus that were ambiguous in a gazetteer lacked a local discriminator in the text. Given the scarcity of human-annotated data, our method used unsupervised machine learning to develop disambiguation rules. Toponyms were automatically tagged with information about them found in a gazetteer. A toponym that was ambiguous in the gazetteer was automatically disambiguated based on preference heuristics. This automatically tagged data was used to train a machine learner, which disambiguated toponyms in a human-annotated news corpus at 78.5% accuracy.}, acmid = {1220621}, address = {Stroudsburg, PA, USA}, author = {Garbin, Eric and Mani, Inderjeet}, booktitle = {Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing}, doi = {10.3115/1220575.1220621}, interhash = {566910cb6e9745ee70da19d2ccafaffa}, intrahash = {de574cf3bff3a3748fcd9bd5a9a0f3d1}, location = {Vancouver, British Columbia, Canada}, numpages = {8}, pages = {363--370}, publisher = {Association for Computational Linguistics}, title = {Disambiguating toponyms in news}, url = {http://dx.doi.org/10.3115/1220575.1220621}, year = 2005 } @inproceedings{fink2009geolocation, abstract = {Understanding the spatial distribution of people who author social media content is of growing interest for researchers and commerce. Blogging platforms depend on authors reporting their own location. However, not all authors report or reveal their location on their blog's home page. Automated geolocation strategies using IP address and domain name are not adequate for determining an author's location because most blogs are not self-hosted. In this paper we describe a method that uses the place name mentions in a blog to determine an author's location. We achieved an accuracy of 63% on a collection of 844 blogs with known locations.}, author = {Fink, C. and Piatko, C. and Mayfield, J. and Chou, D. and Finin, T. and Martineau, J.}, booktitle = {Proceedings of the International Conference on Computational Science and Engineering}, doi = {10.1109/CSE.2009.584}, interhash = {59b768c08026047c20d472ff93a4d513}, intrahash = {70eddd59803db7efee4b8c840fe5a79b}, month = aug, pages = {1088--1092}, title = {The Geolocation of Web Logs from Textual Clues}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5282996}, volume = 4, year = 2009 } @incollection{ireson2010toponym, abstract = {Increasingly user-generated content is being utilised as a source of information, however each individual piece of content tends to contain low levels of information. In addition, such information tends to be informal and imperfect in nature; containing imprecise, subjective, ambiguous expressions. However the content does not have to be interpreted in isolation as it is linked, either explicitly or implicitly, to a network of interrelated content; it may be grouped or tagged with similar content, comments may be added by other users or it may be related to other content posted at the same time or by the same author or members of the author’s social network. This paper generally examines how ambiguous concepts within user-generated content can be assigned a specific/formal meaning by considering the expanding context of the information, i.e. other information contained within directly or indirectly related content, and specifically considers the issue of toponym resolution of locations.}, address = {Berlin/Heidelberg}, affiliation = {University of Sheffield, UK}, author = {Ireson, Neil and Ciravegna, Fabio}, booktitle = {The Semantic Web - ISWC 2010}, doi = {10.1007/978-3-642-17746-0_24}, editor = {Patel-Schneider, Peter and Pan, Yue and Hitzler, Pascal and Mika, Peter and Zhang, Lei and Pan, Jeff and Horrocks, Ian and Glimm, Birte}, interhash = {fd064c5fb724a5a72a6a67d1f6a7f8df}, intrahash = {3d94c7d94945f6e4f37f4391829318e7}, isbn = {978-3-642-17745-3}, keyword = {Computer Science}, pages = {370--385}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Toponym Resolution in Social Media}, url = {http://dx.doi.org/10.1007/978-3-642-17746-0_24}, volume = 6496, year = 2010 }