Multitask Learning for Text Classification with Deep Neural Networks

The classification of the text involving the process of identification and categorization of text is a tedious and a challenging task too. The Capsules Network (Caps-Net) which is a unique architecture with the capability to confiscate the basic attributes comprising the insights of the particular field that could help in bridging the knowledge gap existing between the source and the destination tasks and capability learn more robust representation than the CNN-Convolutional neural networks in the image classification domain is utilized in the paper to classify the text. As the multi –task learning capability enables to part insights between the tasks that are related and enhances data used in training indirectly, the Caps-Net based multi task learning frame work is proposed in the paper. The proposed architecture including the Caps-Net effectively classifies the text and minimizes the interference experienced among the multiple tasks in the multi –task learning. The architecture put forward is evaluated using various text classification dataset ensuring the efficacy of the proffered frame work

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CrowdTC: Crowd-powered Learning for Text Classification

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3457216 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1-23

Author(s):

Keyu Yang ◽

Yunjun Gao ◽

Lei Liang ◽

Song Bian ◽

Lu Chen ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Text Classification ◽

Deep Neural Networks ◽

Semantic Information ◽

Human Beings ◽

Hybrid Neural Network ◽

Public Datasets ◽

Almost All ◽

The Cost

Text classification is a fundamental task in content analysis. Nowadays, deep learning has demonstrated promising performance in text classification compared with shallow models. However, almost all the existing models do not take advantage of the wisdom of human beings to help text classification. Human beings are more intelligent and capable than machine learning models in terms of understanding and capturing the implicit semantic information from text. In this article, we try to take guidance from human beings to classify text. We propose Crowd-powered learning for Text Classification (CrowdTC for short). We design and post the questions on a crowdsourcing platform to extract keywords in text. Sampling and clustering techniques are utilized to reduce the cost of crowdsourcing. Also, we present an attention-based neural network and a hybrid neural network to incorporate the extracted keywords as human guidance into deep neural networks. Extensive experiments on public datasets confirm that CrowdTC improves the text classification accuracy of neural networks by using the crowd-powered keyword guidance.

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Large-Scale Text Classification with Deep Neural Networks

KIISE Transactions on Computing Practices ◽

10.5626/ktcp.2017.23.5.322 ◽

2017 ◽

Vol 23 (5) ◽

pp. 322-327

Author(s):

Hwiyeol Jo ◽

Jin-Hwa Kim ◽

Kyung-Min Kim ◽

Jeong-Ho Chang ◽

Jae-Hong Eom ◽

...

Keyword(s):

Neural Networks ◽

Text Classification ◽

Large Scale ◽

Deep Neural Networks

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Creating, Managing, and Understanding Large, Sparse, Multitask Neural Networks

10.31219/osf.io/bv4qp ◽

2020 ◽

Author(s):

Harshvardhan Sikka

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Text Classification ◽

Multitask Learning ◽

Learning Tasks ◽

Deep Networks ◽

Classification Tasks

One of the popular directions in Deep Learning (DL) research has been to build larger and more complex deep networks that can perform well on several different learning tasks, commonly known as multitask learning. This work is usually done within specific domains, e.g. multitask models that perform captioning, translation, and text classification tasks. Some work has been done in building multimodal/crossmodal networks that use deep networks with a combination of different neural network primitives (Convolutional Layers, Recurrent Layers, Mixture of Expert layers, etc). This paper explores various topics and ideas that may prove relevant to large, sparse, multitask networks and explores the potential for a general approach to building and managing these networks. A framework to automatically build, update, and interpret modular LSMNs is presented in the context of current tooling and theory.

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