The Past, Present and Future of Text Classification

Zechner, Niklas

doi:10.1109/eisic.2013.61

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…As far as simple text classification is concerned, it is an old phenomenon. From the eighteenth century, investigations were conducted to verify the origin of Shakespeare's work [6]. When the first document classification approach was proposed, the process began to emerge into different areas, and as a result, communities began to classify specific types of documents, such as (1) magazines (2) newspapers, etc.…”

Section: Research Backgroundmentioning

confidence: 99%

A Novel Metadata Based Multi-Label Document Classification Technique

Sajid¹,

Ahmad²,

Rahman³

et al. 2023

Computer Systems Science and Engineering

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From the beginning, the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies, its growth rate is overwhelming. On a rough estimate, more than thirty thousand research journals have been issuing around four million papers annually on average. Search engines, indexing services, and digital libraries have been searching for such publications over the web. Nevertheless, getting the most relevant articles against the user requests is yet a fantasy. It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification. To overcome this issue, researchers are striving to investigate new techniques for the classification of the research articles especially, when the complete article text is not available (a case of nonopen access articles). The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess, "to what extent metadata-based features can perform in contrast to content-based approaches." In this regard, novel techniques for investigating multilabel classification have been proposed, developed, and evaluated on metadata such as the Title and Keywords of the articles. The proposed technique has been assessed for two diverse datasets, namely, from the Journal of universal computer science (J.UCS) and the benchmark dataset comprises of the articles published by the Association for computing machinery (ACM). The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature.

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Section: Research Backgroundmentioning

confidence: 99%

A Novel Metadata Based Multi-Label Document Classification Technique

Sajid¹,

Ahmad²,

Rahman³

et al. 2023

Computer Systems Science and Engineering

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show abstract

“…Text classification is a very old dilemma. As early as the 1800s, studies were completed on verifying the authorship of the works of Shakespeare [20]. When the first document classification ap-proach was proposed, thereafter the process started to emerge into different branches.…”

Section: Overview Of State-of-the-art Approachesmentioning

confidence: 99%

Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes

et al. 2023

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Over the decades, a tremendous increase has been witnessed in the production of documents available in digital form. The increased production of documents has gained so much momentum that their rate of production jumps two-fold every five years. These articles are searched over the internet via search engines, digital libraries, and citation indexes. However, the retrieval of relevant research papers for user queries is still a pipedream. This is because scientific documents are not indexed based on some subject classification hierarchies. Hence, the classification of these documents becomes a challenging task for the researchers. Classification of the documents can be two-fold: one way is to assign a single label to each document and the other is to assign multi-labels to each document based on its belonging domains. Classification of the documents can be performed by using either the available metadata or the whole content of the documents. While performing classification, there are many challenges which may belong to the dataset, feature selection technique, preprocessing methodology, and which classification model is suitable for the classification of the documents. This paper highlights the issues for single-label and multi-label classification by using either metadata or content of the documents and why metadata-based approaches are better than content-based approaches in terms of feasibility.

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Hybrid Chinese text classification approach using general knowledge from Baidu Baike

Ren

2016

IEEJ Transactions Elec Engng

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Most of the previous studies focused on enriching text representation to address text classification (TC) task. However, conventional classification approaches with VSM (vector space model) on Chinese text study intensively only the words and their relationship in some specific corpus/dataset but ignore the basic concept of categories and the general knowledge behind the words learned and used to recognize entities by people. This paper focuses on enriching text representation and proposes a novel approach, which complements information from the online Chinese encyclopedia Baidu Baike for Chinese TC. The similarities between every text and each concept of categories and the most related words from Baidu Baike are added to the feature space. The performance of the proposed approach is measured on the Fudan University TC corpus, which is an imbalanced Chinese dataset. In the experiments, the proposed Baidu Baike-based concept similarity approach obtains promising results when compared with a previous research and the conventional method, with macro-precision of 90.31%, recall of 75.45%, and F 1 score 80.32%, which are about 0.02%, 0.15%, 0.12%, respectively, higher than the conventional method, which obviously improves the recall for some small categories while keeping precision at high level and improving the macro F 1 score. Moreover, the proposed approach has good expandability, so that many other knowledge bases could be integrated and many other concepts could be referred to improve the effectiveness.

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The Past, Present and Future of Text Classification

Cited by 5 publications

References 3 publications

A Novel Metadata Based Multi-Label Document Classification Technique

A Novel Metadata Based Multi-Label Document Classification Technique

Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes

Hybrid Chinese text classification approach using general knowledge from Baidu Baike

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