Above are papers related to the following paper: [Paper_ID] Paper #1102873. 	[Title] Regularities in a Random Mapping from Orthography to Semantics [Abstract] In this paper we investigate representational and methodological issues in a attractor network model of the mapping from orthography to semantics based on [Plaut, 1995]. We find that, contrary to psycholinguistic studies, the response time to concrete words (represented by more 1 bits in the output pattern) is slower than for abstract words. This model also predicts that response times to words in a dense semantic neighborhood will be faster than words which have few semantically similar neighbors in the language. This is conceptually consistent with the neighborhood effect seen in the mapping from orthography to phonology [Seidenberg & McClelland, 1989, Plaut et al., 1996] in that patterns with many neighbors are faster in both pathways, but since there is no regularity in the random mapping used here, it is clear that the cause of this effect is different than that of previous experiments. We also report a rather distressing finding. Reaction time in this model is measured by the time it takes the network to settle after being presented with a new input. When the criterion used to determine when the network is settled is changed to include testing of the hidden units, each of the results reported above change the direction of effect abstract words are now slower, as are words in dense semantic neighborhoods. Since there are independent reasons to exclude hidden units from the stopping criterion, and this is what is done in common practice, we believe this phenomenon to be of interest mostly to neural network practitioners. However, it does provide some insight into the interaction between the hidden and output units during settling. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6184. 	[Title] Forward models: Supervised learning with a distal teacher [Abstract] Internal models of the environment have an important role to play in adaptive systems in general and are of particular importance for the supervised learning paradigm. In this paper we demonstrate that certain classical problems associated with the notion of the "teacher" in supervised learning can be solved by judicious use of learned internal models as components of the adaptive system. In particular, we show how supervised learning algorithms can be utilized in cases in which an unknown dynamical system intervenes between actions and desired outcomes. Our approach applies to any supervised learning algorithm that is capable of learning in multi-layer networks. *This paper is a revised version of MIT Center for Cognitive Science Occasional Paper #40. We wish to thank Michael Mozer, Andrew Barto, Robert Jacobs, Eric Loeb, and James McClelland for helpful comments on the manuscript. This project was supported in part by BRSG 2 S07 RR07047-23 awarded by the Biomedical Research Support Grant Program, Division of Research Resources, National Institutes of Health, by a grant from ATR Auditory and Visual Perception Research Laboratories, by a grant from Siemens Corporation, by a grant from the Human Frontier Science Program, and by grant N00014-90-J-1942 awarded by the Office of Naval Research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #23507. 	[Title] Natural Language Processing with Subsymbolic Neural Networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10981. 	[Title] Acquiring the mapping from meaning to sounds [Abstract] 1 We thank Steen Ladegaard Knudsen for his assistance in programming, analysis and running of simulations, Scott Baden for his assistance in vectorizing our code for the Cray Y-MP, the Division of Engineering Block Grant for time on the Cray at the San Diego Supercomputer Center, and the members of the PDPNLP and GURU Research Groups at UCSD for helpful comments on earlier versions of this work. Please summarise all the given information.

Paper #1102873 has the title `Regularities in a Random Mapping from Orthography to Semantics  
` and the abstract `In this paper we investigate representational and methodological issues in a attractor network model of the mapping from orthography to semantics based on [Plaut, 1995]. We find that, contrary to psycholinguistic studies, the response time to concrete words (represented by more 1 bits in the output pattern) is slower than for abstract words. This model also predicts that response times to words in a dense semantic neighborhood will be faster than words which have few semantically similar neighbors in the language. This is conceptually consistent with the neighborhood effect seen in the mapping from orthography to phonology [Seidenberg & McClelland, 1989, Plaut et al., 1996] in that patterns with many neighbors are faster in both pathways, but since there is no regularity in the random mapping used here, it is clear that the cause of this effect is different than that of previous experiments. We also report a rather distressing finding. Reaction time in this model is measured by the time it takes the network to settle after being presented with a new input. When the criterion used to determine when the network is settled is changed to include testing of the hidden units, each of the results reported above change the direction of effect abstract words are now slower, as are words in dense semantic neighborhoods. Since there are independent reasons to exclude hidden units from the stopping criterion, and this is what is done in common practice, we believe this phenomenon to be of interest mostly to neural network practitioners. However, it does provide some insight into the interaction between the hidden and output units during settling. 
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Question: Which category should Paper #1102873 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1102873 has the title `Regularities in a Random Mapping from Orthography to Semantics  
` and the abstract `In this paper we investigate representational and methodological issues in a attractor network model of the mapping from orthography to semantics based on [Plaut, 1995]. We find that, contrary to psycholinguistic studies, the response time to concrete words (represented by more 1 bits in the output pattern) is slower than for abstract words. This model also predicts that response times to words in a dense semantic neighborhood will be faster than words which have few semantically similar neighbors in the language. This is conceptually consistent with the neighborhood effect seen in the mapping from orthography to phonology [Seidenberg & McClelland, 1989, Plaut et al., 1996] in that patterns with many neighbors are faster in both pathways, but since there is no regularity in the random mapping used here, it is clear that the cause of this effect is different than that of previous experiments. We also report a rather distressing finding. Reaction time in this model is measured by the time it takes the network to settle after being presented with a new input. When the criterion used to determine when the network is settled is changed to include testing of the hidden units, each of the results reported above change the direction of effect abstract words are now slower, as are words in dense semantic neighborhoods. Since there are independent reasons to exclude hidden units from the stopping criterion, and this is what is done in common practice, we believe this phenomenon to be of interest mostly to neural network practitioners. However, it does provide some insight into the interaction between the hidden and output units during settling. 
`.
Question: Which category should Paper #1102873 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks