8 research outputs found
Extracting and Visualizing Quotations from News Wires
International audienceWe introduce SAPIENS, a platform for extracting quotations from news wires, associated with their author and context. The originality of SAPIENS is that it relies on a deep linguistic processing chain, which allows for extracting quotations with a wide coverage and an extended definition, including quotations which are only partially quotes-delimited verbatim transcripts. We describe the architecture of SAPIENS and how it was applied to process a corpus of French news wires from the AFP news agency
Convertir des dérivations TAG en dépendances
International audienceLes structures de dĂ©pendances syntaxiques sont importantes et bien adaptĂ©es comme point de dĂ©part de diverses applications. Dans le cadre de l'analyseur TAG FRMG, nous prĂ©sentons les dĂ©tails d'un processus de conversion de forĂȘts partagĂ©es de dĂ©rivations en forĂȘts partagĂ©es de dĂ©pendances. Des Ă©lĂ©ments d'information sont fournis sur un algorithme de dĂ©sambiguisation sur ces forĂȘts de dĂ©pendances
Convertir des dérivations TAG en dépendances
International audienceLes structures de dĂ©pendances syntaxiques sont importantes et bien adaptĂ©es comme point de dĂ©part de diverses applications. Dans le cadre de l'analyseur TAG FRMG, nous prĂ©sentons les dĂ©tails d'un processus de conversion de forĂȘts partagĂ©es de dĂ©rivations en forĂȘts partagĂ©es de dĂ©pendances. Des Ă©lĂ©ments d'information sont fournis sur un algorithme de dĂ©sambiguisation sur ces forĂȘts de dĂ©pendances
Extracting and Attributing Quotes in Text and Assessing them as Opinions
News articles often report on the opinions that salient people have about important issues. While it is possible to infer an opinion from a person's actions, it is much more common to demonstrate that a person holds an opinion by reporting on what they have said. These instances of speech are called reported speech, and in this thesis we set out to detect instances of reported speech, attribute them to their speaker, and to identify which instances provide evidence of an opinion. We first focus on extracting reported speech, which involves finding all acts of communication that are reported in an article. Previous work has approached this task with rule-based methods, however there are several factors that confound these approaches. To demonstrate this, we build a corpus of 965 news articles, where we mark all instances of speech. We then show that a supervised token-based approach outperforms all of our rule-based alternatives, even in extracting direct quotes. Next, we examine the problem of finding the speaker of each quote. For this task we annotate the same 965 news articles with links from each quote to its speaker. Using this, and three other corpora, we develop new methods and features for quote attribution, which achieve state-of-the-art accuracy on our corpus and strong results on the others. Having extracted quotes and determined who spoke them, we move on to the opinion mining part of our work. Most of the task definitions in opinion mining do not easily work with opinions in news, so we define a new task, where the aim is to classify whether quotes demonstrate support, neutrality, or opposition to a given position statement. This formulation improved annotator agreement when compared to our earlier annotation schemes. Using this we build an opinion corpus of 700 news documents covering 7 topics. In this thesis we do not attempt this full task, but we do present preliminary results
Attribution: a computational approach
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