3 resultados para Human Language Technologies

em WestminsterResearch - UK


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Goldin-Meadow (2015) presents an exceptional synthesis of work from studies of children acquiring language under variable circumstances of input or processing abilities. Deaf children who acquire homesign without any well- formed model from which to learn language represent a powerful example. Goldin-Meadow argues that the resilient properties of language that nevertheless emerge include simple syntactic structures, hierarchical organisa- tion, markers modulating the meaning of sentences, and social-communicative functions. Among the fragile or input-dependent properties are the orders that the language follows, the parts into which words are decomposed, and the features that distinguish nominals from predicates. Separation of these two types of properties poses questions concerning the innate constraints on language acquisition (perhaps these equate to the resilient properties) and con‐ cerning the specificity of processes to language (e.g., whether properties such as hierarchical organisation are specific to language or originate in the structure of thought). The study of the resilient properties of human language in the face of adversity and the relation of these properties to the information that is encoded in the human genome represent a research strategy that draws inferences about species universals (properties that all humans share) from data about individual differences (IDs; factors that make humans different from one another). In the following, we suggest three reasons to be cautious about this approach.

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The Human-Computer Interaction (HCI) with interfaces is an active challenge field in the industry over the past decades and has opened the way to communicate with the means of verbal, hand and body gestures using the latest technologies for a variety of different applications in areas such as video games, training and simulation. However, accurate recognition of gestures is still a challenge. In this paper, we review the basic principles and current methodologies used for collecting the raw gesture data from the user for recognize actions the users perform and the technologies currently used for gesture-HCI in games enterprise. In addition, we present a set of projects from various applications in games industry that are using gestural interaction.

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In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understand-ing, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs).