106,244 research outputs found
Genie: A Generator of Natural Language Semantic Parsers for Virtual Assistant Commands
To understand diverse natural language commands, virtual assistants today are
trained with numerous labor-intensive, manually annotated sentences. This paper
presents a methodology and the Genie toolkit that can handle new compound
commands with significantly less manual effort. We advocate formalizing the
capability of virtual assistants with a Virtual Assistant Programming Language
(VAPL) and using a neural semantic parser to translate natural language into
VAPL code. Genie needs only a small realistic set of input sentences for
validating the neural model. Developers write templates to synthesize data;
Genie uses crowdsourced paraphrases and data augmentation, along with the
synthesized data, to train a semantic parser. We also propose design principles
that make VAPL languages amenable to natural language translation. We apply
these principles to revise ThingTalk, the language used by the Almond virtual
assistant. We use Genie to build the first semantic parser that can support
compound virtual assistants commands with unquoted free-form parameters. Genie
achieves a 62% accuracy on realistic user inputs. We demonstrate Genie's
generality by showing a 19% and 31% improvement over the previous state of the
art on a music skill, aggregate functions, and access control.Comment: To appear in PLDI 201
From RESTful Services to RDF: Connecting the Web and the Semantic Web
RESTful services on the Web expose information through retrievable resource
representations that represent self-describing descriptions of resources, and
through the way how these resources are interlinked through the hyperlinks that
can be found in those representations. This basic design of RESTful services
means that for extracting the most useful information from a service, it is
necessary to understand a service's representations, which means both the
semantics in terms of describing a resource, and also its semantics in terms of
describing its linkage with other resources. Based on the Resource Linking
Language (ReLL), this paper describes a framework for how RESTful services can
be described, and how these descriptions can then be used to harvest
information from these services. Building on this framework, a layered model of
RESTful service semantics allows to represent a service's information in
RDF/OWL. Because REST is based on the linkage between resources, the same model
can be used for aggregating and interlinking multiple services for extracting
RDF data from sets of RESTful services
Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature
Over the past 50 years many have debated what representation should be used
to capture the meaning of natural language utterances. Recently new needs of
such representations have been raised in research. Here I survey some of the
interesting representations suggested to answer for these new needs.Comment: 15 pages, no figure
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
The Semantic Web: Apotheosis of annotation, but what are its semantics?
This article discusses what kind of entity the proposed Semantic Web (SW) is, principally by reference to the relationship of natural language structure to knowledge representation (KR). There are three distinct views on this issue. The first is that the SW is basically a renaming of the traditional AI KR task, with all its problems and challenges. The second view is that the SW will be, at a minimum, the World Wide Web with its constituent documents annotated so as to yield their content, or meaning structure, more directly. This view makes natural language processing central as the procedural bridge from texts to KR, usually via some form of automated information extraction. The third view is that the SW is about trusted databases as the foundation of a system of Web processes and services. There's also a fourth view, which is much more difficult to define and discuss: If the SW just keeps moving as an engineering development and is lucky, then real problems won't arise. This article is part of a special issue called Semantic Web Update
Trustworthy Refactoring via Decomposition and Schemes: A Complex Case Study
Widely used complex code refactoring tools lack a solid reasoning about the
correctness of the transformations they implement, whilst interest in proven
correct refactoring is ever increasing as only formal verification can provide
true confidence in applying tool-automated refactoring to industrial-scale
code. By using our strategic rewriting based refactoring specification
language, we present the decomposition of a complex transformation into smaller
steps that can be expressed as instances of refactoring schemes, then we
demonstrate the semi-automatic formal verification of the components based on a
theoretical understanding of the semantics of the programming language. The
extensible and verifiable refactoring definitions can be executed in our
interpreter built on top of a static analyser framework.Comment: In Proceedings VPT 2017, arXiv:1708.0688
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