The Experts below are selected from a list of 83268 Experts worldwide ranked by ideXlab platform
Noburo Saji - One of the best experts on this subject based on the ideXlab platform.
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Evolution of Verb Meanings in Children and L2 Adult Learners Through Reorganization of an Entire Semantic Domain: The Case of Chinese Carry/Hold Verbs
Scientific Studies of Reading, 2015Co-Authors: Noburo Saji, Mutsumi ImaiAbstract:The meaning of a word is not acquired in isolation from other words. This article investigates how first-language (L1) and adult second-language (L2) learners of Chinese learn the meanings of verbs belonging to the same Semantic Domain, focusing on the Semantic Domain of “carrying/holding” in Chinese. Results revealed that the verb use of L2 adults is heavily influenced by their lexical knowledge of L1 and that their development of word meanings stops before they fully reach the adult native speakers' word meaning. L1 children in contrast tend to depend on perceptually visible features of actions at the initial stage of lexical acquisition and then gradually learn how their L1 categorizes the actions by verbs. We argue that L2 learners need to attain meta-knowledge about the mapping of the entire configuration of the corresponding lexical Domain between L1 and L2 and discuss how reading inside and outside of the classroom could foster this process.
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Word learning does not end at fast-mapping: Evolution of verb meanings through reorganization of an entire Semantic Domain
Cognition, 2010Co-Authors: Noburo Saji, Mutsumi Imai, Henrik Saalbach, Yuping Zhang, Hua Shu, Hiroyuki OkadaAbstract:This paper explores the process through which children sort out the relations among verbs belonging to the same Semantic Domain. Using a set of Chinese verbs denoting a range of action events that are labeled by carrying or holding in English as a test case, we looked at how Chinese-speaking 3-, 5-, and 7-year-olds and adults apply 13 different verbs to a range of carrying/holding events. We asked how children learning Chinese originally divide and label the Semantic space in this Domain, how they discover the boundaries between different words, and how the meanings of verbs in the Domain as a whole evolve toward the representations of adults. We also addressed the question of what factors make verb meaning acquisition easy or hard. Results showed that the pattern of children's verb use is largely different from that of adults and that it takes a long time for children to be able to use all verbs in this Domain in the way adults do. We also found that children start to use broad-covering and frequent verbs the earliest, but use of these verbs tends to converge on adult use more slowly because children could not use these verbs as adults did until they had identified boundaries between these verbs and other near-synonyms with more specific meanings. This research highlights the importance of systematic investigation of words that belong to the same Domain as a whole, examining how word meanings in a Domain develop as parts of a connected system, instead of examining each word on its own: learning the meaning of a verb invites restructuring of the meanings of related, neighboring verbs.
Mutsumi Imai - One of the best experts on this subject based on the ideXlab platform.
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Evolution of Verb Meanings in Children and L2 Adult Learners Through Reorganization of an Entire Semantic Domain: The Case of Chinese Carry/Hold Verbs
Scientific Studies of Reading, 2015Co-Authors: Noburo Saji, Mutsumi ImaiAbstract:The meaning of a word is not acquired in isolation from other words. This article investigates how first-language (L1) and adult second-language (L2) learners of Chinese learn the meanings of verbs belonging to the same Semantic Domain, focusing on the Semantic Domain of “carrying/holding” in Chinese. Results revealed that the verb use of L2 adults is heavily influenced by their lexical knowledge of L1 and that their development of word meanings stops before they fully reach the adult native speakers' word meaning. L1 children in contrast tend to depend on perceptually visible features of actions at the initial stage of lexical acquisition and then gradually learn how their L1 categorizes the actions by verbs. We argue that L2 learners need to attain meta-knowledge about the mapping of the entire configuration of the corresponding lexical Domain between L1 and L2 and discuss how reading inside and outside of the classroom could foster this process.
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Word learning does not end at fast-mapping: Evolution of verb meanings through reorganization of an entire Semantic Domain
Cognition, 2010Co-Authors: Noburo Saji, Mutsumi Imai, Henrik Saalbach, Yuping Zhang, Hua Shu, Hiroyuki OkadaAbstract:This paper explores the process through which children sort out the relations among verbs belonging to the same Semantic Domain. Using a set of Chinese verbs denoting a range of action events that are labeled by carrying or holding in English as a test case, we looked at how Chinese-speaking 3-, 5-, and 7-year-olds and adults apply 13 different verbs to a range of carrying/holding events. We asked how children learning Chinese originally divide and label the Semantic space in this Domain, how they discover the boundaries between different words, and how the meanings of verbs in the Domain as a whole evolve toward the representations of adults. We also addressed the question of what factors make verb meaning acquisition easy or hard. Results showed that the pattern of children's verb use is largely different from that of adults and that it takes a long time for children to be able to use all verbs in this Domain in the way adults do. We also found that children start to use broad-covering and frequent verbs the earliest, but use of these verbs tends to converge on adult use more slowly because children could not use these verbs as adults did until they had identified boundaries between these verbs and other near-synonyms with more specific meanings. This research highlights the importance of systematic investigation of words that belong to the same Domain as a whole, examining how word meanings in a Domain develop as parts of a connected system, instead of examining each word on its own: learning the meaning of a verb invites restructuring of the meanings of related, neighboring verbs.
Reiko Heckel - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Meta Modeling with time: Specifying the Semantics of multimedia sequence diagrams
Software & Systems Modeling, 2004Co-Authors: Jan Hendrik Hausmann, Reiko Heckel, Stefan SauerAbstract:The Unified Modeling Langugage (UML) offers different diagram types to model the behavior of software systems. In some Domains like embedded real-time systems or multimedia systems, it is necessary to include specifications of time in behavioral models since the correctness of these applications depends on the fulfillment of temporal requirements in addition to functional requirements. UML thus already incorporates language features to model time and temporal constraints. Such model elements must have an equivalent in the Semantic Domain. We have proposed Dynamic Meta Modeling (DMM), an approach based on graph transformation, as a means for specifying operational Semantics of dynamic UML diagrams. In this article, we extend this approach to also account for time by extending the Semantic Domain to timed graph transformation. This enables us to define the operational Semantics of UML diagrams with time specifications. As an example, we provide Semantics for special sequence diagrams from the Domain of multimedia application modeling.
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AGTIVE - Design of an Agent-Oriented Modeling Language Based on Graph Transformation
Applications of Graph Transformations with Industrial Relevance, 2004Co-Authors: Ralph Depke, Jan Hendrik Hausmann, Reiko HeckelAbstract:The use of UML extension mechanisms for the definition of an Agent-Oriented Modeling Language only fixes its syntax. But agent concepts demand an appropriate Semantics for a visual modeling language. Graphs have been shown to constitute a precise and general Semantic Domain for visual modeling languages. The question is how agent concepts can be systematically represented in the Semantic Domain and further on be expressed by appropriate UML diagrams. We propose a language architecture based on the Semantic Domain of graphs and elements of the concrete syntax of UML. We use the proposed language architecture to define parts of an agent-oriented modeling language.
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Design of an agent-oriented modeling language based on graph transformation
Lecture Notes in Computer Science, 2004Co-Authors: Ralph Depke, Jan Hendrik Hausmann, Reiko HeckelAbstract:The use of UML extension mechanisms for the definition of an Agent-Oriented Modeling Language only fixes its syntax. But agent concepts demand an appropriate Semantics for a visual modeling language. Graphs have been shown to constitute a precise and general Semantic Domain for visual modeling languages. The question is how agent concepts can be systematically represented in the Semantic Domain and further on be expressed by appropriate UML diagrams. We propose a language architecture based on the Semantic Domain of graphs and elements of the concrete syntax of UML. We use the proposed language architecture to define parts of an agent-oriented modeling language.
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Towards Automatic Translation of UML Models into Semantic Domains
ETAPS 2002 : European joint conference on theory and practice of software. Satellite workshop, 2002Co-Authors: Reiko Heckel, Jochen M Küster, Gabriele TaentzerAbstract:The use of UML for software specification leads usually to lots of diagrams showing different aspects and components of the software system in several views. In order to support a view-oriented approach to system modeling, consistency in views and in between views has to be manageable. It is a reasonable approach to consistency management when first choosing a suitable Semantic Domain, provide a partial mapping into this Domain, and specify as well as verify consistency constraints formulated in that Domain. Annotated meta model rules can be used to translate elements of UML models into the Semantic Domain chosen. In this contribution, we consider triple graph grammars and attributed graph transformation approaches for the precise definition of meta model rules and outline the tool support for automatic translation.
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Behavioral Constraints for Visual Models
Electronic Notes in Theoretical Computer Science, 2001Co-Authors: Reiko Heckel, Jochen M KüsterAbstract:Abstract In this paper, we discuss the issue of consistency of behavioral models in the UML and present techniques for specifying and analyzing consistency. Using meta-model rules we transform elements of UML models into a Semantic Domain. Then, consistency constraints can by specified and validated using the language and the tools of the Semantic Domain. This general methodology is exemplified by the problem of protocol statechart inheritance.
James Ferryman - One of the best experts on this subject based on the ideXlab platform.
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meeting detection in video through Semantic analysis
Advanced Video and Signal Based Surveillance, 2015Co-Authors: Luis Patino, James FerrymanAbstract:In this paper we present a novel approach to detect people meeting. The proposed approach works by translating people behaviour from trajectory information into Semantic terms. Having available a Semantic model of the meeting behaviour, the event detection is performed in the Semantic Domain. The model is learnt employing a soft-computing clustering algorithm that combines trajectory information and motion Semantic terms. A stable representation can be obtained from a series of examples. Results obtained on a series of videos with different types of meeting situations show that the proposed approach can learn a generic model that can effectively be applied on the behaviour recognition of meeting situations.
Hiroyuki Okada - One of the best experts on this subject based on the ideXlab platform.
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Word learning does not end at fast-mapping: Evolution of verb meanings through reorganization of an entire Semantic Domain
Cognition, 2010Co-Authors: Noburo Saji, Mutsumi Imai, Henrik Saalbach, Yuping Zhang, Hua Shu, Hiroyuki OkadaAbstract:This paper explores the process through which children sort out the relations among verbs belonging to the same Semantic Domain. Using a set of Chinese verbs denoting a range of action events that are labeled by carrying or holding in English as a test case, we looked at how Chinese-speaking 3-, 5-, and 7-year-olds and adults apply 13 different verbs to a range of carrying/holding events. We asked how children learning Chinese originally divide and label the Semantic space in this Domain, how they discover the boundaries between different words, and how the meanings of verbs in the Domain as a whole evolve toward the representations of adults. We also addressed the question of what factors make verb meaning acquisition easy or hard. Results showed that the pattern of children's verb use is largely different from that of adults and that it takes a long time for children to be able to use all verbs in this Domain in the way adults do. We also found that children start to use broad-covering and frequent verbs the earliest, but use of these verbs tends to converge on adult use more slowly because children could not use these verbs as adults did until they had identified boundaries between these verbs and other near-synonyms with more specific meanings. This research highlights the importance of systematic investigation of words that belong to the same Domain as a whole, examining how word meanings in a Domain develop as parts of a connected system, instead of examining each word on its own: learning the meaning of a verb invites restructuring of the meanings of related, neighboring verbs.