91,459 research outputs found
Lexical acquisition in elementary science classes
The purpose of this study was to further researchers' understanding of lexical acquisition in the beginning primary schoolchild by investigating word learning in small-group elementary science classes. Two experiments were conducted to examine the role of semantic scaffolding (e.g., use of synonymous terms) and physical scaffolding (e.g., pointing to referents) in children's acquisition of novel property terms. Children's lexical knowledge was assessed using multiple tasks (naming, comprehension, and definitional). Children struggled to acquire meanings of adjectives without semantic or physical scaffolding (Experiment 1), but they were successful in acquiring extensive lexical knowledge when offered semantic scaffolding (Experiment 2). Experiment 2 also shows that semantic scaffolding used in combination with physical scaffolding helped children acquire novel adjectives and that children who correctly named pictures of adjectives had acquired definitions
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Automatic extraction of paraphrastic phrases from medium size corpora
This paper presents a versatile system intended to acquire paraphrastic
phrases from a representative corpus. In order to decrease the time spent on
the elaboration of resources for NLP system (for example Information
Extraction, IE hereafter), we suggest to use a machine learning system that
helps defining new templates and associated resources. This knowledge is
automatically derived from the text collection, in interaction with a large
semantic network
Developing domain ontologies for course content
Ontologies have the potential to play an important role in instructional design and the development of course content. They can be used to represent knowledge about content, supporting instructors in creating content or learners in accessing content in a knowledge-guided way. While ontologies exist for many subject domains, their quality and suitability for the educational context might be unclear. For numerous subjects, ontologies do not exist. We present a method for domain experts rather than ontology engineers to develop ontologies for use in the delivery of courseware content. We will focus in particular on relationship types that allow us to model rich domains adequately
Neurally Implementable Semantic Networks
We propose general principles for semantic networks allowing them to be
implemented as dynamical neural networks. Major features of our scheme include:
(a) the interpretation that each node in a network stands for a bound
integration of the meanings of all nodes and external events the node links
with; (b) the systematic use of nodes that stand for categories or types, with
separate nodes for instances of these types; (c) an implementation of
relationships that does not use intrinsically typed links between nodes.Comment: 32 pages, 12 figure
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