7 research outputs found
SiSSA: An Infrastructure for Developing NLP Applications
No full textIn recent years there has been a growing interest in the commercial deployment of NLP technologies. This paper presents SiSSA, a project whose main aim is that of developing an infrastructure for prototyping, editing and validation of NLP application architectures. The system will provide the user with a graphical environment for (1) selecting the NLP activities relevant for the particular NLP task and the associated linguistic processors that execute them; (2) connecting new linguistic processors to SiSSA; (3) checking that the chosen architectural hypothesis corresponds to the functional specifications of the given application. The proposed infrastructure makes crucial use of state-of-the-art software technologies (CORBA, XML, RDF) to integrate different linguistic processors in an effective way. In the paper the definition of a metaformalism for the unification of different formalisms for grammar description is also briefly presented
SiSSA - An Infrastructure for NLP Application Development
No full textRecently there has been a growing interest in infrastructures for sharing NLP tools and resources. This paper presents SiSSA, a project that aims at developing an infrastructure for prototyping, editing and validation of NLP application architectures. The system will provide the user with a graphical environment for (1) selecting the NLP activities relevant for the particular NLP task and the associated linguistic processors that execute them; (2) connecting new linguistic processors to SiSSA; (3) checking that the chosen architectural hypothesis corresponds to the functional specifications of the given application
SiSSA: A Software Infrastructure for Developing Distributed NLP Applications
No full textIn recent years there has been a growing interest in the commercial deployment of NLP technologies. This paper presents SiSSA, a project that aims at developing a system for prototyping, editing and validation of NLP application architectures. The system will provide the user with a graphical environment for (1) selecting the NLP activities relevant for the particular NLP task and the associated linguistic processors that execute them; (2) connecting new linguistic processors to SiSSA; (3) checking that the chosen architectural hypothesis corresponds to the functional specifications of the given application
A speech understanding and dialog system with a homogeneous linguistic knowledge base
No full textMast M, Kummert F, Ehrlich U, et al. A speech understanding and dialog system with a homogeneous linguistic knowledge base. IEEE transactions on pattern analysis and machine intelligence. 1994;16(2):179-194.This article presents the speech understanding and dialog system EVAR. All levels of linguistic knowledge are used both to control the analysis process and for the interpretation of an utterance. All kinds of knowledge are integrated in a homogeneous knowledge base. The control algorithm used for the analysis is defined within the representation scheme and does not depend on the application. One of the aims of EVAR is to develop a system structure where linguistic and non-linguistic expectations could be used not only for the interpretation but also as predictions for the recognition process
Attribution: a computational approach
No full textOur 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
