IEEE Transactions on Pattern Analysis and Machine Intelligence

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Digital Design And Computer Architecture

An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools

In the present research era, machine learning is an important and unavoidable zone where it provides better solutions to various domains. In particular deep learning is one of the cost efficient, effective supervised learning model, which can be applied to various complicated issues. Since deep learning has various illustrative features and it doesn’t depend on any limited learning methods which helps to obtain better solutions. As deep learning has significant performance and advancements it is widely used in various applications like image classification, face recognition, visual recognition, language processing, speech recognition, object detection and various science, business analysis, etc., This survey work mainly provides an insight about deep learning through an intensive analysis of deep learning architectures and its characteristics along with its limitations. Also, this research work analyses recent trends in deep learning through various literatures to explore the present evolution in deep learning models.

https://www.irojournals.com/jscp/V2/I3/06.pdf

Survey on Neural Network Architectures with Deep Learning

Evolutionary deep learning: A survey

Enhancing deep neural networks via multiple kernel learning

An essential task for the design of Question Answering systems is the selection of the sentence containing (or constituting) the answer from documents relevant to the asked question. Previous neural models have experimented with using additional text together with the target sentence to learn a selection function but these methods were not powerful enough to effectively encode contextual information. In this paper, we analyze the role of contextual information for the sentence selection task in Transformer based architectures, leveraging two types of context, local and global. The former describes the paragraph containing the sentence, aiming at solving implicit references, whereas the latter describes the entire document containing the candidate sentence, providing content-based information. The results on three different benchmarks show that the combination of the local and global context in a Transformer model significantly improves the accuracy in Answer Sentence Selection.

/pdf/answer-sentence-selection-using-local-and-global-context-in-293stdr289.pdf

Answer Sentence Selection Using Local and Global Context in Transformer Models

In recent years, the increasing interest in the development of automatic approaches for unmasking deception in online sources led to promising results. Nonetheless, among the others, two major issues remain still unsolved: the stability of classifiers performances across different domains and languages. Tackling these issues is challenging since labelled corpora involving multiple domains and compiled in more than one language are few in the scientific literature. For filling this gap, in this paper we introduce DecOp (Deceptive Opinions), a new language resource developed for automatic deception detection in cross-domain and cross-language scenarios. DecOp is composed of 5000 examples of both truthful and deceitful first-person opinions balanced both across five different domains and two languages and, to the best of our knowledge, is the largest corpus allowing cross-domain and cross-language comparisons in deceit detection tasks. In this paper, we describe the collection procedure of the DecOp corpus and his main characteristics. Moreover, the human performance on the DecOp test-set and preliminary experiments by means of machine learning models based on Transformer architecture are shown.

DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text.

It is known that any dot-product kernel can be seen as a linear non-negative combination of homogeneous polynomial kernels. In this paper, we demonstrate that, under mild conditions, any dot-product kernel defined on binary valued data can be seen as a linear non-negative combination of boolean kernels, specifically, monotone conjunctive kernels (mC-kernels) with different degrees. We also propose a new radius-margin based multiple kernel learning (MKL) algorithm to learn the parameters of the combination. An empirical analysis of the MKL weights distribution shows that our method is able to give solutions which are more sparse and effective compared to the ones of state-of-the-art margin-based MKL methods. The empirical analysis have been performed on eleven UCI categorical datasets.

Ivano Lauriola

Papers

An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools

Enhancing deep neural networks via multiple kernel learning

Answer Sentence Selection Using Local and Global Context in Transformer Models

DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text.

Radius-Margin Ratio Optimization for Dot-Product Boolean Kernel Learning