Papers › Tag Recommendation for Online Q&A Communities based on BERT Pre-Training Technique

Tag Recommendation for Online Q&A Communities based on BERT Pre-Training Technique

10 Oct 2020arXiv:2010.04971archive 2025-07-28

Navid Khezrian, Jafar Habibi, Issa Annamoradnejad

Online Q&A and open source communities use tags and keywords to index, categorize, and search for specific content. The most obvious advantage of tag recommendation is the correct classification of information. In this study, we used the BERT pre-training technique in tag recommendation task for online Q&A and open-source communities for the first time. Our evaluation on freecode datasets show that the proposed method, called TagBERT, is more accurate compared to deep learning and other baseline methods. Moreover, our model achieved a high stability by solving the problem of previous researches, where increasing the number of tag recommendations significantly reduced model performance.

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Code

Moradnejad/Bert-Based-Tag-Recommendation officialmentioned on GitHub report

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Tasks

Multi-Label Text Classification TAG Text Classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Multi-Label Text Classification	Freecode	TagBERT	F1-score	46	#1 of 5	Archive leaderboard	report
Multi-Label Text Classification	Freecode	TagCNN	F1-score	45.3	#2 of 5	Archive leaderboard	report
Multi-Label Text Classification	Freecode	TagMulRec	F1-score	36.4	#3 of 5	Archive leaderboard	report
Multi-Label Text Classification	Freecode	EnTagRec	F1-score	36	#4 of 5	Archive leaderboard	report
Multi-Label Text Classification	Freecode	FastTagRec	F1-score	33.2	#5 of 5	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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