Are Deep Learning Approaches Suitable for Natural Language Processing?

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).

A question of tomorrow: Blanchot, surrealism and the time of the fragment

This article offers a close critical reading of Blanchot's essay on Surrealism, 'Tomorrow at Stake', raising a series of questions concerning the time of 'Surrealist experience' and its relation to those temporal structures inscribed within the concepts of modernity, the avant-garde, and (art) history itself. It is argued, through a posited connection to Romantic conceptions of the fragment, that those 'reflexes of the future' which for Surrealism determine the value of the present, may be understood, philosophically, in relation to diverse conceptions of the 'openness' of the question, in turn suggesting a more complex understanding of Surrealism's 'avant-garde' character.