19,377 research outputs found
Do You See What I Mean? Visual Resolution of Linguistic Ambiguities
Understanding language goes hand in hand with the ability to integrate
complex contextual information obtained via perception. In this work, we
present a novel task for grounded language understanding: disambiguating a
sentence given a visual scene which depicts one of the possible interpretations
of that sentence. To this end, we introduce a new multimodal corpus containing
ambiguous sentences, representing a wide range of syntactic, semantic and
discourse ambiguities, coupled with videos that visualize the different
interpretations for each sentence. We address this task by extending a vision
model which determines if a sentence is depicted by a video. We demonstrate how
such a model can be adjusted to recognize different interpretations of the same
underlying sentence, allowing to disambiguate sentences in a unified fashion
across the different ambiguity types.Comment: EMNLP 201
Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art
Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover
Textual Economy through Close Coupling of Syntax and Semantics
We focus on the production of efficient descriptions of objects, actions and
events. We define a type of efficiency, textual economy, that exploits the
hearer's recognition of inferential links to material elsewhere within a
sentence. Textual economy leads to efficient descriptions because the material
that supports such inferences has been included to satisfy independent
communicative goals, and is therefore overloaded in Pollack's sense. We argue
that achieving textual economy imposes strong requirements on the
representation and reasoning used in generating sentences. The representation
must support the generator's simultaneous consideration of syntax and
semantics. Reasoning must enable the generator to assess quickly and reliably
at any stage how the hearer will interpret the current sentence, with its
(incomplete) syntax and semantics. We show that these representational and
reasoning requirements are met in the SPUD system for sentence planning and
realization.Comment: 10 pages, uses QobiTree.te
Detecting Functional Requirements Inconsistencies within Multi-teams Projects Framed into a Model-based Web Methodology
One of the most essential processes within the software project life cycle is the REP (Requirements
Engineering Process) because it allows specifying the software product requirements. This specification
should be as consistent as possible because it allows estimating in a suitable manner the effort required to
obtain the final product. REP is complex in itself, but this complexity is greatly increased in big, distributed
and heterogeneous projects with multiple analyst teams and high integration between functional modules.
This paper presents an approach for the systematic conciliation of functional requirements in big projects
dealing with a web model-based approach and how this approach may be implemented in the context of the
NDT (Navigational Development Techniques): a web methodology. This paper also describes the empirical
evaluation in the CALIPSOneo project by analyzing the improvements obtained with our approach.Ministerio de EconomĂa y Competitividad TIN2013-46928-C3-3-RMinisterio de EconomĂa y Competitividad TIN2015-71938-RED
Ontologies and Information Extraction
This report argues that, even in the simplest cases, IE is an ontology-driven
process. It is not a mere text filtering method based on simple pattern
matching and keywords, because the extracted pieces of texts are interpreted
with respect to a predefined partial domain model. This report shows that
depending on the nature and the depth of the interpretation to be done for
extracting the information, more or less knowledge must be involved. This
report is mainly illustrated in biology, a domain in which there are critical
needs for content-based exploration of the scientific literature and which
becomes a major application domain for IE
Techniques and potential capabilities of multi-resolutional information (knowledge) processing
A concept of nested hierarchical (multi-resolutional, pyramidal) information (knowledge) processing is introduced for a variety of systems including data and/or knowledge bases, vision, control, and manufacturing systems, industrial automated robots, and (self-programmed) autonomous intelligent machines. A set of practical recommendations is presented using a case study of a multiresolutional object representation. It is demonstrated here that any intelligent module transforms (sometimes, irreversibly) the knowledge it deals with, and this tranformation affects the subsequent computation processes, e.g., those of decision and control. Several types of knowledge transformation are reviewed. Definite conditions are analyzed, satisfaction of which is required for organization and processing of redundant information (knowledge) in the multi-resolutional systems. Providing a definite degree of redundancy is one of these conditions
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