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Language Planning as a Sociolinguistic Experiment

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The importance of Henk Zeevat's new monograph cannot be overstated. [...] I recommend it to anyone who combines interests in language, logic, and computation [...]. David Beaver, University of Texas at Austin

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Dissertation Information

Title: 'Toponym Resolution in Text: Annotation, evaluation and applications of spatial grounding of place names' Add Dissertation
Author: Jochen Leidner Update Dissertation
Email: click here to access email
Homepage: ''
Degree Awarded: 'University of Edinburgh' , 'School of Informatics '
Completed in:
Linguistic Subfield(s): 'Computational Linguistics' 'Discourse Analysis' 'Pragmatics' 'Text/Corpus Linguistics'
Subject Language(s): 'English'
Director(s): Claire Grover
Bonnie Webber

Abstract: Background: Spatial and temporal expressions refer to events in
space-time, and the grounding of events is a precondition for
reasoning. Thus, automatic grounding can improve many applications
such as automatic map drawing and question answering (e.g., for
questions like 'How far is London from Edinburgh?'). Whereas temporal
grounding has received considerable attention, robust spatial
grounding has long been neglected. I define the task of automatic
Toponym Resolution as computing the mapping from instances of names
for places as found in a text to a representation of the extensional
semantics of the location referred to, such as a geographic
latitude/longitude footprint. The mapping between names and locations
is referentially ambiguous: London can refer to the capital of the UK
or to London, Ontario, Canada, or other Londons on earth).

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.

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 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 cataloged in the inventory. I
then compare the performance of the same TR algorithms under three
different conditions, namely applying it to the output of human
named entity annotation, automatic annotation using an existing
Maximum Entropy sequence tagging model, and a naive 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
and Recall evaluation metrics.

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.
- 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, and a comparison between a replicated method as described
in the literature, which functions as a baseline, and a novel
algorithm based on minimality heuristics; and
- an empirical analysis of the relative utility of various heuristic
biases and other sources of evidence.