# On the Use of Knowledge Transfer Techniques for Biomedical Named Entity Recognition +
## 3 Experiments  
Our experiments were performed on 15 datasets which were also used by [PERSON] et al. [35] and [PERSON] et al. [34]. The bioentities in these datasets were disease, species, cell component, cell, gene/protein, and chemical. Every dataset included training, validation, and test sets. Detailed descriptions of the datasets used in the experiments can be found in Appendix A. We followed the experimental setup analogous to [PERSON] et al. ([[https://github.com/yuzhimanhua/Multi-BioNER](https://github.com/yuzhimanhua/Multi-BioNER)]([https://github.com/yuzhimanhua/Multi-BioNER](https://github.com/yuzhimanhua/Multi-BioNER)), accessed on 1 February 2023 ), where both training and validation datasets were used for training the model. We used the IOBES tagging format [52], where I stands for inside-entity when the token occurs inside the entity span, O (outside-entity) represents tokens that do not belong to the entity class, B represents the beginning of an entity, E (end-entity) indicates the last token of the entity span, and S (single-entity) is used when the entity consists of a single token. For performance evaluation of the model, we used the (macroaveraged) F1-score metric (i.e., each class was considered equally important), as it is the most widely adopted for named entity recognition tasks [34; 35; 53]. As it is usual for named entity recognition tasks, the evaluation basically did not take into account the recognition of single tokens but rather that of the whole entity. More specifically, we evaluated the model performance using the macroaveraged F1-score since it allowed us to avoid biases towards the majority class. The macroaveraged F1-scores reported in the following sections represent the average scores of 10 runs. Henceforth, when mentioning F1-score, we mean the macroaveraged F1-score. For the Tables presented in the later sections, we show the best results with bold font. Moreover, we utilized a domain-specific pretrained word embedding to avoid a high rate of out-of-vocabulary words problem. The WikiPubMed-PMC word embedding was used, which was trained on a huge quantity of data from PubMedCentral(PMC) articles and PubMed abstracts as well as on English Wikipedia articles [54], whereas the character-level embedding was initialized randomly, and the orthographic-level (case) embedding was described by the identity matrix in which the existence of a word's orthographic feature was defined with one in the diagonal of a matrix. We also investigated different hypotheses extending the experiments on our proposed models. The first hypothesis concerned the effect of auxiliary tasks on the main task. The MTM-CNN was trained with similar BioNER auxiliary tasks while, in another experiment, the proposed model was additionally trained along with the dissimilar auxiliary tasks which were GENIA-POS tagging and CoNLL chunking. We also investigated the appropriate layers where effective supervision and complete feature representation from the auxiliary task could be accomplished (see Table 4). For this reason, the auxiliary tasks were trained at the earlier layers (innermost layer in Figure 3) as well as at the later layers (outermost layer in Figure 3). Training at the earlier layers of the model did not include a task-specific BiLSTM layer for the auxiliary tasks while the main task was extended with a task-specific BiLSTM. The empirical results and statistical analysis showed that auxiliary tasks were more beneficial when used at the inner layer. This indicated that there was hierarchical learning between different tasks in the MTL approach. The experiments for the Embeddings from Language Models (ELMo) implementation was based on AllenNLP ([[https://github.com/allenai/allennlp](https://github.com/allenai/allennlp)]([https://github.com/allenai/allennlp](https://github.com/allenai/allennlp)), accessed on 1 February 2023) and used a pretrained ELMo model trained on PubMed data. The LSTM unit size was 4096 and the output dimension was 512.