Journal ArticleDOI
Deep neural network for hierarchical extreme multi-label text classification
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TLDR
An analysis of a Deep Learning architecture devoted to text classification, considering the extreme multi-class and multi-label text classification problem, when a hierarchical label set is defined and a methodology named Hierarchical Label Set Expansion (HLSE) is presented.About:
This article is published in Applied Soft Computing.The article was published on 2019-06-01. It has received 86 citations till now. The article focuses on the topics: Word embedding & Automatic summarization.read more
Citations
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Journal ArticleDOI
Cryptocurrency malware hunting: A deep Recurrent Neural Network approach
Abbas Yazdinejad,Hamed HaddadPajouh,Ali Dehghantanha,Reza M. Parizi,Gautam Srivastava,Gautam Srivastava,Mu Yen Chen +6 more
TL;DR: This paper proposes a novel deep Recurrent Neural Network ( RNN) learning model that utilizes the RNN to analyze Windows applications’ operation codes (Opcodes) as a case study and applies traditional machine learning classifiers to show the applicability of deep learners ( LSTM ) versus traditional models in dealing with cryptocurrency malware.
Journal ArticleDOI
A Compact Convolutional Neural Network Augmented with Multiscale Feature Extraction of Acquired Monitoring Data for Mechanical Intelligent Fault Diagnosis
TL;DR: A compact convolutional neural network augmented with multiscale feature extraction unit is introduced to extract features at different time scales without adding convolution layers, which can reduce the depth of the network while ensuring classification ability and alleviating the overfitting problem caused by the network being too complicated.
Does BERT Make Any Sense? Interpretable Word Sense Disambiguation with Contextualized Embeddings
TL;DR: A simple but effective approach to WSD using a nearest neighbor classification on CWEs and it is shown that the pre-trained BERT model is able to place polysemic words into distinct 'sense' regions of the embedding space, while ELMo and Flair NLP do not seem to possess this ability.
Journal ArticleDOI
A multi-label text classification method via dynamic semantic representation model and deep neural network
TL;DR: A novel multi-label text classification method that combines dynamic semantic representation model and deep neural network (DSRM-DNN) is proposed that outperforms the state-of-the-art methods.
Journal ArticleDOI
Feature selection for label distribution learning via feature similarity and label correlation
TL;DR: Experimental results indicate that the proposed feature selection algorithm for label distribution learning was more effective than five state-of-art feature selection algorithms on twelve datasets, with respect to six representative evaluation measures.
References
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Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Posted Content
Efficient Estimation of Word Representations in Vector Space
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
Book
Introduction to Information Retrieval
TL;DR: In this article, the authors present an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.
Posted Content
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: In this paper, the Skip-gram model is used to learn high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships and improve both the quality of the vectors and the training speed.
Journal Article
Statistical Comparisons of Classifiers over Multiple Data Sets
TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.