Keyword-Based Semi-Supervised Text Classification

Severin, Karl; Gokhale, Swapna S.; Dagnino, Aldo

doi:10.1109/compsac.2019.00067

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…Zhao et al [16] improved the traditional naive Bayes classifier to achieve semisupervised text classification. Severin et al [17] surpassed the random forest model by extracting keyword features.…”

Section: Text Classification Methodsmentioning

confidence: 99%

Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network

Yin

Wang

2022

Advances in Multimedia

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With the development of Internet technology, government affairs can be handled online. More and more citizens are using online platforms to report to government departments, which is generating a lot of textual data. Among them, the basic but important problem is to automatically classify the different categories of messages, so that staff from different departments can process relevant information quickly. However, government messages have problems such as fast update rate, a large amount of information, long texts, and difficulty in capturing key points, which make supervised learning methods unsuitable for processing such texts. To address these problems, we propose a semisupervised text classification method based on a transformer-based pointer generator network named Ptr4BERT, which uses the pointer generator network with BERT(bidirectional encoder representation from transformers) embedding as a preprocessor for feature extraction. In this method, text classification can achieve very good results with a small set of labeled data, by extracting features exclusively from the message text. In order to verify the effect of our proposed model, we performed some experiments. Besides, we designed a crawler program and obtained two datasets from different websites, which are named HNMes and QDMes. Experimental results have shown that the proposed method outperforms the state-of-the-art methods significantly.

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Section: Text Classification Methodsmentioning

confidence: 99%

Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network

Yin

Wang

2022

Advances in Multimedia

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“…For instance, Ref. [23] adopted semi-supervised learning on keywords to analyze a corpus of unstructured text documents regarding accounts receivable disputes at a large corporation. According to the authors, the semi-supervised approach reduced the manual effort of labeling time by 50%.…”

Section: Semi-supervised Learningmentioning

confidence: 99%

“…Graph Search [12][13][14][15][16] [12, 17-19] [6,8,20,21] [ [23][24][25][26] [ [27][28][29][30] Our Proposal…”

Section: Semi-supervisedmentioning

confidence: 99%

Semi-Supervised Classification with A*: A Case Study on Electronic Invoicing

Panichi,

Lazzeri

2023

BDCC

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This paper addresses the time-intensive task of assigning accurate account labels to invoice entries within corporate bookkeeping. Despite the advent of electronic invoicing, many software solutions still rely on rule-based approaches that fail to address the multifaceted nature of this challenge. While machine learning holds promise for such repetitive tasks, the presence of low-quality training data often poses a hurdle. Frequently, labels pertain to invoice rows at a group level rather than an individual level, leading to the exclusion of numerous records during preprocessing. To enhance the efficiency of an invoice entry classifier within a semi-supervised context, this study proposes an innovative approach that combines the classifier with the A* graph search algorithm. Through experimentation across various classifiers, the results consistently demonstrated a noteworthy increase in accuracy, ranging between 1% and 4%. This improvement is primarily attributed to a marked reduction in the discard rate of data, which decreased from 39% to 14%. This paper contributes to the literature by presenting a method that leverages the synergy of a classifier and A* graph search to overcome challenges posed by limited and group-level label information in the realm of electronic invoicing classification.

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A review of semi-supervised learning for text classification

Duarte

Berton

2023

Artif Intell Rev

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A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To perform this task we usually need a large set of labeled data that can be expensive, time-consuming, or difficult to be obtained. Considering this scenario semi-supervised learning (SSL), the branch of machine learning concerned with using labeled and unlabeled data has expanded in volume and scope. Since no recent survey exists to overview how SSL has been used in text classification, we aim to fill this gap and present an up-to-date review of SSL for text classification. We retrieve 1794 works from the last 5 years from IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Then, 157 articles were selected to be included in this review. We present the application domain, datasets, and languages employed in the works. The text representations and machine learning algorithms. We also summarize and organize the works following a recent taxonomy of SSL. We analyze the percentage of labeled data used, the evaluation metrics, and obtained results. Lastly, we present some limitations and future trends in the area. We aim to provide researchers and practitioners with an outline of the area as well as useful information for their current research.

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Keyword-Based Semi-Supervised Text Classification

Cited by 4 publications

References 10 publications

Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network

Ptr4BERT: Automatic Semisupervised Chinese Government Message Text Classification Method Based on Transformer-Based Pointer Generator Network

Semi-Supervised Classification with A*: A Case Study on Electronic Invoicing

A review of semi-supervised learning for text classification

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