A multi-view method of scientific paper classification via heterogeneous graph embeddings

Lv, Yiqin; Xie, Zheng; Zuo, Xiaojing; Song, Yiping

doi:10.1007/s11192-022-04419-1

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…In a very recent research work [23] the authors have used graph embeddings in scientific paper classification, taking advantage of the knowledge graphs that have been made behind the collaboration between co-authors boosting the classification accuracy to 98%. However, they assume that the scientific papers are already listed in the major literature databases and author information is available and without ambiguity.…”

Section: Classification Of Scientific Documentsmentioning

confidence: 99%

Classification of Scientific Documents in the Kazakh Language Using Deep Neural Networks and a Fusion of Images and Text

Bogdanchikov

Ayazbayev

Varlamis

2022

BDCC

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The rapid development of natural language processing and deep learning techniques has boosted the performance of related algorithms in several linguistic and text mining tasks. Consequently, applications such as opinion mining, fake news detection or document classification that assign documents to predefined categories have significantly benefited from pre-trained language models, word or sentence embeddings, linguistic corpora, knowledge graphs and other resources that are in abundance for the more popular languages (e.g., English, Chinese, etc.). Less represented languages, such as the Kazakh language, balkan languages, etc., still lack the necessary linguistic resources and thus the performance of the respective methods is still low. In this work, we develop a model that classifies scientific papers written in the Kazakh language using both text and image information and demonstrate that this fusion of information can be beneficial for cases of languages that have limited resources for machine learning models’ training. With this fusion, we improve the classification accuracy by 4.4499% compared to the models that use only text or only image information. The successful use of the proposed method in scientific documents’ classification paves the way for more complex classification models and more application in other domains such as news classification, sentiment analysis, etc., in the Kazakh language.

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Section: Classification Of Scientific Documentsmentioning

confidence: 99%