Attribution: a computational approach
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Date
26/11/2015Author
Pareti, Silvia
Metadata
Abstract
Our society is overwhelmed with an ever growing amount of information. Effective
management of this information requires novel ways to filter and select the most relevant
pieces of information. Some of this information can be associated with the source
or sources expressing it. Sources and their relation to what they express affect information
and whether we perceive it as relevant, biased or truthful. In news texts in
particular, it is common practice to report third-party statements and opinions. Recognizing
relations of attribution is therefore a necessary step toward detecting statements
and opinions of specific sources and selecting and evaluating information on the basis
of its source.
The automatic identification of Attribution Relations has applications in numerous
research areas. Quotation and opinion extraction, discourse and factuality have
all partly addressed the annotation and identification of Attribution Relations. However,
disjoint efforts have provided a partial and partly inaccurate picture of attribution.
Moreover, these research efforts have generated small or incomplete resources, thus
limiting the applicability of machine learning approaches. Existing approaches to extract
Attribution Relations have focused on rule-based models, which are limited both
in coverage and precision.
This thesis presents a computational approach to attribution that recasts attribution
extraction as the identification of the attributed text, its source and the lexical cue linking
them in a relation. Drawing on preliminary data-driven investigation, I present a
comprehensive lexicalised approach to attribution and further refine and test a previously
defined annotation scheme. The scheme has been used to create a corpus annotated
with Attribution Relations, with the goal of contributing a large and complete
resource than can lay the foundations for future attribution studies.
Based on this resource, I developed a system for the automatic extraction of attribution
relations that surpasses traditional syntactic pattern-based approaches. The system
is a pipeline of classification and sequence labelling models that identify and link each
of the components of an attribution relation. The results show concrete opportunities
for attribution-based applications.