The Experts below are selected from a list of 7353 Experts worldwide ranked by ideXlab platform
Merrill F. Garrett - One of the best experts on this subject based on the ideXlab platform.
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representing the meanings of object and action words the featural and unitary Semantic Space hypothesis
Cognitive Psychology, 2004Co-Authors: Gabriella Vigliocco, David P. Vinson, William Lewis, Merrill F. GarrettAbstract:This paper presents the Featural and Unitary Semantic Space (FUSS) hypothesis of the meanings of object and action words. The hypothesis, implemented in a statistical model, is based on the following assumptions: First, it is assumed that the meanings of words are grounded in conceptual featural representations, some of which are organized according to modality. Second, it is assumed that conceptual featural representations are bound into lexico-Semantic representations that provide an interface between conceptual knowledge and other linguistic information (syntax and phonology). Finally, the FUSS model employs the same principles and tools for objects and actions, modeling both domains in a single Semantic Space. We assess the plausibility of the model by showing that it can capture generalizations presented in the literature, in particular those related to category-related deficits, and show that it can predict Semantic effects in behavioral experiments for object and action words better than other models such as Latent Semantic Analysis (Landauer & Dumais, 1997) and similarity metrics derived from Wordnet (Miller & Fellbaum, 1991).
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Representing the meanings of object and action words: The featural and unitary Semantic Space hypothesis
Cognitive Psychology, 2004Co-Authors: Gabriella Vigliocco, David P. Vinson, William Lewis, Merrill F. GarrettAbstract:This paper presents the Featural and Unitary Semantic Space (FUSS) hypothesis of the meanings of object and action words. The hypothesis, implemented in a statistical model, is based on the following assumptions: First, it is assumed that the meanings of words are grounded in conceptual featural representations, some of which are organized according to modality. Second, it is assumed that conceptual featural representations are bound into lexico-Semantic representations that provide an interface between conceptual knowledge and other linguistic information (syntax and phonology). Finally, the FUSS model employs the same principles and tools for objects and actions, modeling both domains in a single Semantic Space. We assess the plausibility of the model by showing that it can capture generalizations presented in the literature, in particular those related to category-related deficits, and show that it can predict Semantic effects in behavioral experiments for object and action words better than other models such as Latent Semantic Analysis (Lndauer & Dumais, 1997) and similarity metrics derived from Wordnet (Miller & Fellbaum, 1991). © 2003 Elsevier Inc. All rights reserved.
Mark Sandler - One of the best experts on this subject based on the ideXlab platform.
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a Semantic Space for music derived from social tags
International Symposium Conference on Music Information Retrieval, 2007Co-Authors: Mark Levy, Mark SandlerAbstract:In this paper we investigate social tags as a novel highvolume source of Semantic metadata for music, using techniques from the fields of information retrieval and multivariate data analysis. We show that, despite the ad hoc and informal language of tagging, tags define a low-dimensional Semantic Space that is extremely well-behaved at the track level, in particular being highly organised by artist and musical genre. We introduce the use of Correspondence Analysis to visualise this Semantic Space, and show how it can be applied to create a browse-by-mood interface for a psychologically-motivated two-dimensional subSpace representing musical emotion.
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ISMIR - A Semantic Space FOR MUSIC DERIVED FROM SOCIAL TAGS
2007Co-Authors: Mark Levy, Mark SandlerAbstract:In this paper we investigate social tags as a novel highvolume source of Semantic metadata for music, using techniques from the fields of information retrieval and multivariate data analysis. We show that, despite the ad hoc and informal language of tagging, tags define a low-dimensional Semantic Space that is extremely well-behaved at the track level, in particular being highly organised by artist and musical genre. We introduce the use of Correspondence Analysis to visualise this Semantic Space, and show how it can be applied to create a browse-by-mood interface for a psychologically-motivated two-dimensional subSpace representing musical emotion.
Qi Tian - One of the best experts on this subject based on the ideXlab platform.
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structured weak Semantic Space construction for visual categorization
IEEE Transactions on Neural Networks, 2018Co-Authors: Chunjie Zhang, Jian Cheng, Qi TianAbstract:Visual features have been widely used for image representation and categorization. However, visual features are often inconsistent with human perception. Besides, constructing explicit Semantic Space is still an open problem. To alleviate these two problems, in this paper, we propose to construct structured weak Semantic Space for image representation. Exemplar classifier is first trained to separate each training image from other images for weak Semantic Space construction. However, each exemplar classifier separates one training image from other images, and it only has limited Semantic separability. Besides, the outputs of exemplar classifiers are inconsistent with each other. We jointly construct the weak Semantic Space using structured constraint. This is achieved by imposing low-rank constraint on the outputs of exemplar classifiers with sparsity constraint. An alternative optimization procedure is used to learn the exemplar classifiers. Since the proposed method does not dependent on the initial image representation strategy, we can make use of various visual features for efficient exemplar classifier training (e.g., fisher vector-based methods and convolutional neural networks-based methods). We apply the proposed structured weak Semantic Space-based image representation method for categorization. The experimental results on several public image data sets prove the effectiveness of the proposed method.
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boosted random contextual Semantic Space based representation for visual recognition
Information Sciences, 2016Co-Authors: Chunjie Zhang, Qingming Huang, Huanian Wang, Qi TianAbstract:We propose an image representation method using boosted random contextual Semantic Spaces.The proposed BRCSS can alleviate the Semantic gap by using Semantic Space based image representation.BRCSS uses the visual representation of images in an iterative way with random sampling and re-weighting.Experimental results demonstrate the effectiveness and efficiency of the proposed method. Visual information has been widely used for image representation. Although proven very effective, the visual representation lacks explicit Semantics. However, how to generate a proper Semantic Space for image representation is still an open problem that needs to be solved. To jointly model the visual and Semantic representations of images, we propose a boosted random contextual Semantic Space based image representation method. Images are initially represented using local feature's distribution histograms. The Semantic Space is generated by randomly selecting training images. Images are then mapped into the Semantic Space accordingly. Semantic context is explored to model the correlations of different Semantics which is then used for classification. The classification results are used to re-weight training images in a boosted way. The re-weighted images are used to construct new Semantic Space for classification. In this way, we are able to jointly consider the visual and Semantic information of images. Image classification experiments on several public datasets show the effectiveness of the proposed method.
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object categorization in sub Semantic Space
Neurocomputing, 2014Co-Authors: Chunjie Zhang, Jian Cheng, Junbiao Pang, Chao Liang, Qingming Huang, Qi TianAbstract:Due to the Semantic gap, the low-level features are unsatisfactory for object categorization. Besides, the use of Semantic related image representation may not be able to cope with large inter-class variations and is not very robust to noise. To solve these problems, in this paper, we propose a novel object categorization method by using the sub-Semantic Space based image representation. First, exemplar classifiers are trained by separating each training image from the others and serve as the weak Semantic similarity measurement. Then a graph is constructed by combining the visual similarity and weak Semantic similarity of these training images. We partition this graph into visually and Semantically similar sub-sets. Each sub-set of images is then used to train classifiers in order to separate this sub-set from the others. The learned sub-set classifiers are then used to construct a sub-Semantic Space based representation of images. This sub-Semantic Space is not only more Semantically meaningful than exemplar based representation but also more reliable and resistant to noise than traditional Semantic Space based image representation. Finally, we make categorization of objects using this sub-Semantic Space with a structure regularized SVM classifier and conduct experiments on several public datasets to demonstrate the effectiveness of the proposed method.
Gabriella Vigliocco - One of the best experts on this subject based on the ideXlab platform.
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representing the meanings of object and action words the featural and unitary Semantic Space hypothesis
Cognitive Psychology, 2004Co-Authors: Gabriella Vigliocco, David P. Vinson, William Lewis, Merrill F. GarrettAbstract:This paper presents the Featural and Unitary Semantic Space (FUSS) hypothesis of the meanings of object and action words. The hypothesis, implemented in a statistical model, is based on the following assumptions: First, it is assumed that the meanings of words are grounded in conceptual featural representations, some of which are organized according to modality. Second, it is assumed that conceptual featural representations are bound into lexico-Semantic representations that provide an interface between conceptual knowledge and other linguistic information (syntax and phonology). Finally, the FUSS model employs the same principles and tools for objects and actions, modeling both domains in a single Semantic Space. We assess the plausibility of the model by showing that it can capture generalizations presented in the literature, in particular those related to category-related deficits, and show that it can predict Semantic effects in behavioral experiments for object and action words better than other models such as Latent Semantic Analysis (Landauer & Dumais, 1997) and similarity metrics derived from Wordnet (Miller & Fellbaum, 1991).
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Representing the meanings of object and action words: The featural and unitary Semantic Space hypothesis
Cognitive Psychology, 2004Co-Authors: Gabriella Vigliocco, David P. Vinson, William Lewis, Merrill F. GarrettAbstract:This paper presents the Featural and Unitary Semantic Space (FUSS) hypothesis of the meanings of object and action words. The hypothesis, implemented in a statistical model, is based on the following assumptions: First, it is assumed that the meanings of words are grounded in conceptual featural representations, some of which are organized according to modality. Second, it is assumed that conceptual featural representations are bound into lexico-Semantic representations that provide an interface between conceptual knowledge and other linguistic information (syntax and phonology). Finally, the FUSS model employs the same principles and tools for objects and actions, modeling both domains in a single Semantic Space. We assess the plausibility of the model by showing that it can capture generalizations presented in the literature, in particular those related to category-related deficits, and show that it can predict Semantic effects in behavioral experiments for object and action words better than other models such as Latent Semantic Analysis (Lndauer & Dumais, 1997) and similarity metrics derived from Wordnet (Miller & Fellbaum, 1991). © 2003 Elsevier Inc. All rights reserved.
Douglas J.k. Mewhort - One of the best experts on this subject based on the ideXlab platform.
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high dimensional Semantic Space accounts of priming
Journal of Memory and Language, 2006Co-Authors: Michael N. Jones, Walter Kintsch, Douglas J.k. MewhortAbstract:A broad range of priming data has been used to explore the structure of Semantic memory and to test between models of word representation. In this paper, we examine the computational mechanisms required to learn distributed Semantic representations for words directly from unsupervised experience with language. To best account for the variety of priming data, we introduce a holographic model of the lexicon that learns word meaning and order information from experience with a large text corpus. Both context and order information are learned into the same composite representation by simple summation and convolution mechanisms (cf. Murdock, B.B. (1982). A theory for the storage and retrieval of item and associative information. Psychological Review, 89, 609–626). We compare the similarity structure of representations learned by the holographic model, Latent Semantic Analysis (LSA; Landauer, T.K., & Dumais, S.T. (1997). A solution to Plato’s problem: The latent Semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104, 211–240), and the HyperSpace Analogue to Language (HAL; Lund, K., & Burgess, C., (1996). Producing high-dimensional Semantic Spaces from lexical co-occurrence. Behavior Research Methods, Instrumentation, and Computers, 28, 203–208) at predicting human data in a variety of Semantic, associated, and mediated priming experiments. We found that both word context and word order information are necessary to account for trends in the human data. The representations learned from the holographic system incorporate both types of structure, and are shown to account for priming phenomena across several tasks.
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High-dimensional Semantic Space accounts of priming
Journal of Memory and Language, 2006Co-Authors: Michael N. Jones, Walter Kintsch, Douglas J.k. MewhortAbstract:A broad range of priming data has been used to explore the structure of Semantic memory and to test between models of word representation. In this paper, we examine the computational mechanisms required to learn distributed Semantic representations for words directly from unsupervised experience with language. To best account for the variety of priming data, we introduce a holographic model of the lexicon that learns word meaning and order information from experience with a large text corpus. Both context and order information are learned into the same composite representation by simple summation and convolution mechanisms (cf. Murdock, B.B. (1982). A theory for the storage and retrieval of item and associative information. Psychological Review, 89, 609-626). We compare the similarity structure of representations learned by the holographic model, Latent Semantic Analysis (LSA; Landauer, T.K., & Dumais, S.T. (1997). A solution to Plato's problem: The latent Semantic analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104, 211-240), and the HyperSpace Analogue to Language (HAL; Lund, K., & Burgess, C., (1996). Producing high-dimensional Semantic Spaces from lexical co-occurrence. Behavior Research Methods, Instrumentation, and Computers, 28, 203-208) at predicting human data in a variety of Semantic, associated, and mediated priming experiments. We found that both word context and word order information are necessary to account for trends in the human data. The representations learned from the holographic system incorporate both types of structure, and are shown to account for priming phenomena across several tasks. © 2006 Elsevier Inc. All rights reserved.
