-The explanation of methods in some paragraphs is too detailed and there is no mention of other work and it is repeated in the corresponding method sections, the authors committed to address this issue in the final version.
-README file for the dataset [Authors committed to add README file] - General Discussion: - Section 2.2 mentions examples of DBpedia properties that were used as features. Do the authors mean that all the properties have been used or there is a subset? If the latter please list them. In the authors' response, the authors explain in more details this point and I strongly believe that it is crucial to list all the features in details in the final version for clarity and replicability of the paper.
- In section 2.3 the authors use Lample et al. Bi-LSTM-CRF model, it might be beneficial to add that the input is word embeddings (similarly to Lample et al.) - Figure 3, KNs in source language or in English? ( since the mentions have been translated to English). In the authors' response, the authors stated that they will correct the figure.
- Based on section 2.4 it seems that topical relatedness implies that some features are domain dependent. It would be helpful to see how much domain dependent features affect the performance. In the final version, the authors will add the performance results for the above mentioned features, as mentioned in their response.
- In related work, the authors make a strong connection to Sil and Florian work where they emphasize the supervised vs. unsupervised difference. The proposed approach is still supervised in the sense of training, however the generation of training data doesn’t involve human interference
- In section 2.3 the authors use Lample et al. Bi-LSTM-CRF model, it might be beneficial to add that the input is word embeddings (similarly to Lample et al.) - Figure 3, KNs in source language or in English? ( since the mentions have been translated to English). In the authors' response, the authors stated that they will correct the figure.