# Active Learning for Biomedical Article Classification with Bag of Words and _FastText_ Embeddings
### Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Representations** & **Algorithms** & **AL Setups** & **Evaluation** & **Data** \\ \hline
[16] & BOW, LDA & SVM & initialization: unknown, query: uncertainty, committee; stopping: none & utility, coverage, ROC AUC; train–test split train+pool evaluation & 3 medical and 4 social science SLR studies \\ \hline
[17] & BOW (TF-IDF) & SVM & initialization: random; query: uncertainty (custom); stopping: none & accuracy; train–test split WebKB \\ \hline
[18] & BOW (1- and 2-gram) & regex classifier, NB, SVM, BERT & initialization: random; query: uncertainty (custom); stopping: query score variance & accuracy, precision F1; \(k\)-fold CV & 3 biomedical datasets \\ \hline
[19] & BOW (TF-IDF) & NB & initialization: random; query: minority; stopping: none & WSS, RRF; train+pool evaluation & 2 biomedical SLR studies, 1 software fault prediction SLR study, 1 longitudinal SLR study \\ \hline
[20] & BOW & SVM, NB, LR & initialization: random; query: margin, committee; stopping: none & F1; train–test split Wikipedia talk pages (toxic comments), Breibart news none \\ \hline
[21] — & BERT & initialization: random; query: uncertainty (custom); stopping: none & accuracy; train–test split Treebank, KvK-Frontpages \\ \hline
[22] & BOW (TF-IDF), doc2 vec RF & SVM, NB, LR, RF & initialization: random; query: minority; stopping: none & WSS, RRF, TD; train+pool evaluation & 6 biomedical SLR studies \\ \hline
[23] & BOW (TF-IDF), sBERT & CNN, SVM, RF, LR, NB & initialization: random; query: minority; stopping: none & WSS, TD, recall; train+pool evaluation & 1 biomedical SLR study \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._probability or decision value predictions, although the latter would clearly provide a more complete and useful assessment of the model's predictive utility. Prior research specifically addressing the application of active learning to creating text classification models for systematic literature reviews usually focuses on SLR-specific performance measures. This is the case for [19; 22; 23], which use the _work saved over sampling_ (WSS) metric [24], as well as _time to discovery_ (TD) and _relevant records found_ (RRF). These studies do not use \(k\)-fold cross-validation or train-test splitting, but rather evaluate the performance on the entire dataset, combining known true class labels (for articles labeled as relevant or irrelevant by the human evaluator) and model predictions (for unlabeled articles). This scheme is referred to as train+pool evaluation in Table 1. By the way, these three studies employ the _ASReview_ system for systematic reviews [19] and report results obtained with text representations, algorithms, and AL setups available there. The row for [19] in Table 1 only describes the scope of the demonstration experiments reported in the article, but ASRreview functionality is considerably broader. While most of the related prior research used conventional machine learning algorithms, there have been also attempts to use deep learning models to create text classification models, including the BERT language model fine-tuned for classification [21] or a convolutional neural network [23]. While the former achieved good classification quality, it exhibited only a rather limited improvement during active learning. The latter generally worked on par with conventional algorithms.