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Hierarchical Neural Network Approaches for Long Document Classification [article]

Snehal Khandve, Vedangi Wagh, Apurva Wani, Isha Joshi, Raviraj Joshi
2022 arXiv   pre-print
In this paper, we explore hierarchical transfer learning approaches for long document classification.  ...  Along with the hierarchical approaches, this work also provides a comparison of different deep learning algorithms like USE, BERT, HAN, Longformer, and BigBird for long document classification.  ...  We would like to express our gratitude towards our mentors at L3Cube for their continuous support and encouragement.  ... 
arXiv:2201.06774v1 fatcat:tlx2zldiefb2rbsrwof7z4zena

A Hierarchical Neural-Network-Based Document Representation Approach for Text Classification

Jianming Zheng, Yupu Guo, Chong Feng, Honghui Chen
2018 Mathematical Problems in Engineering  
In particular, we incorporate the hierarchical architecture into three traditional neural-network models for document representation, resulting in three hierarchical neural representation models for document  ...  In addition, we find that the long documents benefit more from the hierarchical architecture than the short ones as the improvement in terms of accuracy on long documents is greater than that on short  ...  models without hierarchical architecture for document classification.  ... 
doi:10.1155/2018/7987691 fatcat:psxo3mpqsjcltbte7rs3scnbge

Hierarchical Text Classification of Urdu News using Deep Neural Network [article]

Taimoor Ahmed Javed, Waseem Shahzad, Umair Arshad
2021 arXiv   pre-print
The result shows that our proposed method is very effective for hierarchical text classification and it outperforms baseline methods significantly and also achieved good results as compare to deep neural  ...  To classify large size of corpus, one common approach is to use hierarchical text classification, which aims to classify textual data in a hierarchical structure.  ...  In this paper, approaches to the hierarchy of the classification of documents advance the concept of deep neural learning network.  ... 
arXiv:2107.03141v1 fatcat:kxrtu6i7ongzrfcotihjvqvtdu

HDLTex: Hierarchical Deep Learning for Text Classification

Kamran Kowsari, Donald E. Brown, Mojtaba Heidarysafa, Kiana Jafari Meimandi, Matthew S. Gerber, Laura E. Barnes
2017 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA)  
Instead we perform hierarchical classification using an approach we call Hierarchical Deep Learning for Text classification (HDLTex).  ...  This paper approaches this problem differently from current document classification methods that view the problem as multi-class classification.  ...  Convolutional Neural Networks (CNN) The final deep learning approach we developed for hierarchical document classification is the Convolutional Neural Network (CNN).  ... 
doi:10.1109/icmla.2017.0-134 dblp:conf/icmla/KowsariBHMGB17 fatcat:rj6nx4cwa5bfndeqof7fiwqtze

Knowledge-oriented Hierarchical Neural Network for Sentiment Classification

Yanliu Wang, Pengfei Li
2019 IOP Conference Series: Materials Science and Engineering  
To address the issues, we design a model that combines Knowledge-oriented Convolutional Neural Network (K-CNN) and bidirectional Gated Recurrent Neural Network (biGRU) in a hierarchical way for sentiment  ...  Experiments on two datasets show that our model outperforms other classical deep neural network models.  ...  Conclusion In this paper, we propose a Knowledge-oriented Hierarchical Neural Network (KHNN) for sentiment classification, where a document is modelled from sentence-level to document-level.  ... 
doi:10.1088/1757-899x/646/1/012023 fatcat:7bzzqrb2hbfqdfyainrmhbopru

Hierarchical Attentional Hybrid Neural Networks for Document Classification [article]

Jader Abreu, Luis Fred, David Macêdo, Cleber Zanchettin
2019 arXiv   pre-print
In this paper, we propose a new approach based on convolutional neural networks, gated recurrent units and attention mechanisms for document classification tasks.  ...  Document classification is a challenging task with important applications. Deep learning approaches to the problem have gained much attention.  ...  HIERARCHICAL ATTENTIONAL HYBRID NEURAL NETWORKS The HAHNN model combines convolutional neural networks, gated recurrent unit, and attention mechanisms to document classification.  ... 
arXiv:1901.06610v1 fatcat:fcfbp2mw3re63grbuglpcevb3e

Genre Identification and the Compositional Effect of Genre in Literature

Joseph Worsham, Jugal Kalita
2018 International Conference on Computational Linguistics  
The task is to assign the literary classification to a full-length book belonging to a corpus of literature, where the works on average are well over 200,000 words long and genre is an abstract thematic  ...  In this paper, we address the problem of developing approaches which are capable of working with extremely large and complex literary documents to perform Genre Identification.  ...  Using multiple levels of this mechanism, a Hierarchical Attention Network has been proposed for the task of document classification (Yang et al., 2016) .  ... 
dblp:conf/coling/WorshamK18 fatcat:lde4buolh5aonatyw7akbmfvuu

Hierarchical Hybrid Neural Networks With Multi-Head Attention for Document Classification

Weihao Huang, Jiaojiao Chen, Qianhua Cai, Xuejie Liu, Yudong Zhang, Xiaohui Hu
2022 International Journal of Data Warehousing and Mining  
To this end, this paper proposes a hierarchical hybrid neural network with multi-head attention (HHNN-MHA) model on the task of document classification.  ...  Document classification is a research topic aiming to predict the overall text sentiment polarity with the advent of deep neural networks.  ...  State-of-the-art document classification approaches are typically dominated by two distinguishing neural networks: the convolutional neural network (CNN), and the recurrent neural network (RNN).  ... 
doi:10.4018/ijdwm.303673 fatcat:s7rzpdnzgvdqnd5f3fnpzn7fhq

Initializing neural networks for hierarchical multi-label text classification

Simon Baker, Anna Korhonen
2017 BioNLP 2017  
In this paper, we apply a new method for hierarchical multi-label text classification that initializes a neural network model final hidden layer such that it leverages label co-occurrence relations such  ...  We evaluated this approach using two hierarchical multi-label text classification tasks in the biomedical domain using both sentence-and document-level classification.  ...  We thank Tyler Griffiths for his help in proofreading and editing this paper.  ... 
doi:10.18653/v1/w17-2339 dblp:conf/bionlp/BakerK17 fatcat:w3zgslph2jemzoaz6xjwgzpj3m

RMDL

Kamran Kowsari, Mojtaba Heidarysafa, Donald E. Brown, Kiana Jafari Meimandi, Laura E. Barnes
2018 Proceedings of the 2nd International Conference on Information System and Data Mining - ICISDM '18  
This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification.  ...  These test results show that RDML produces consistently better performance than standard methods over a broad range of data types and classification problems.  ...  Yang et. al. in 2016 [53] developed hierarchical attention networks for document classification.  ... 
doi:10.1145/3206098.3206111 dblp:conf/icisdm/KowsariHBMB18 fatcat:lzyx7ze67vgu5lv2jbguion544

HARC-New Hybrid Method with Hierarchical Attention Based Bidirectional Recurrent Neural Network with Dilated Convolutional Neural Network to Recognize Multilabel Emotions from Text

Md Shofiqul Islam, Mst Sunjida Sultana, Mr Uttam Kumar, Jubayer Al Mahmud, SM Jahidul Islam
2021 Jurnal Ilmiah Teknik Elektro Komputer dan Informatika  
In this analysis, the proposed new hybrid deep learning HARC model architecture for the recognition of multilevel textual sentiment that combines hierarchical attention with Convolutional Neural Network  ...  The main goals of this proposed technique are to use deep learning approaches to identify multilevel textual sentiment with far less time and more accurate and simple network structure training for better  ...  In recent years, a variety of neural network models have developed with better outcomes than other approaches, especially for video classification [3] , speech recognition [4] , text classification  ... 
doi:10.26555/jiteki.v7i1.20550 fatcat:xnes4y6p2fawfcfiuxecfcu2ru

Examining Attention Mechanisms in Deep Learning Models for Sentiment Analysis

Spyridon Kardakis, Isidoros Perikos, Foteini Grivokostopoulou, Ioannis Hatzilygeroudis
2021 Applied Sciences  
Attention-based methods for deep neural networks constitute a technique that has attracted increased interest in recent years.  ...  A comparative analysis is performed in a text sentiment classification task where baseline models are compared with and without the use of attention for every experiment.  ...  [29] propose a model named hierarchical attention network (HAN) for a document classification task that mirrors the hierarchical structure of documents.  ... 
doi:10.3390/app11093883 doaj:b86149fafb844c5cb45ae309527024c6 fatcat:2z6zve3piffyfl4nhbepoqjkwe

MEXN: Multi-Stage Extraction Network for Patent Document Classification

Juho Bai, Inwook Shim, Seog Park
2020 Applied Sciences  
To address this issue, we propose a neural network-based document classification for patent documents by designing a novel multi-stage feature extraction network (MEXN), which comprise of paragraphs encoder  ...  The patent document has different content for each paragraph, and the length of the document is also very long. Moreover, patent documents are classified hierarchically as multi-labels.  ...  We are excited about the future of our model to be applied to other formal document classification tasks.  ... 
doi:10.3390/app10186229 fatcat:tdyahkeqc5f4rhikq5yaz4qnzi

ProSeqo: Projection Sequence Networks for On-Device Text Classification

Zornitsa Kozareva, Sujith Ravi
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
This results in fast and compact neural networks that can perform on-device inference for complex short and long text classification tasks.  ...  Results show that ProSeqo outperformed state-of-the-art neural and on-device approaches for short text classification tasks such as dialog act and intent prediction.  ...  for long document classification of news, answers and product reviews.  ... 
doi:10.18653/v1/d19-1402 dblp:conf/emnlp/KozarevaR19 fatcat:iugtv4ls7bbqfm4m4rrpy4qdyi

PRADO: Projection Attention Networks for Document Classification On-Device

Karthik Krishnamoorthi, Sujith Ravi, Zornitsa Kozareva
2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)  
Using this approach, we train tiny neural networks just 200 Kilobytes in size that improve over prior CNN and LSTM models and achieve near state of the art performance on multiple long document classification  ...  We evaluate our approach on multiple large document text classification tasks.  ...  for long text classification.  ... 
doi:10.18653/v1/d19-1506 dblp:conf/emnlp/KrishnamoorthiR19 fatcat:p5ditgrnf5hlrmbwx2p7zssbdi
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