All reviews of published articles are made public. This includes manuscript files, peer review comments, author rebuttals and revised materials. Note: This was optional for articles submitted before 13 February 2023.
Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the article is rejected). If the article is accepted, then reviewers who provided their name will be associated with the article itself.
Thanks for your response, it is my pleasure to inform you that the amended paper is accepted, thanks for your efforts.
The form of the manuscript looks good at present.
The experimental design of this presented study is credible.
The scientific contribution of this paper has been well demonstrated.
Congratulations!
The manuscript has been moderately revised according to the previous reviews. I have no other comments.
I have no other comments.
I have no other comments.
I have no other comments.
Dear Authors,
Thanks for your efforts to respond to the reviewers' comments, but there are still some issues that still need some improvements. Could you please check the reviewers' feedback?
The quality of this manuscript has apparently improved. Kindly suggest revising the current title of this manuscript as "A comparative analysis on question classification task based on deep learning approaches".
I have no more questions.
The current work supports their aims.
Thanks for carefully addressing my concerns.
English writing has been improved, but many issues still exist.
Just to name a few randomly:
Line 267: GRU store[s] and filter[s]
Line 326: to extrcut [extract]
Line 341 we proposed [a] deep learning hybrid architecture
I'd suggest proofreading and revising the manuscript seriously.
It has been moderately improved.
It has been moderately improved.
Although there was an overall improvement there are still some language issues with the document. examples L97 covert, L141 to understand to apply, L212 - we evaluated to explain L284 - repeated text.
The inflected/agglutinative issue was also not clarified, there is no explanation of these terms that are used in the text (although inflected now appears only in the abstract). A simple definition would be enough.
The structure was slightly improved although there is still are still explanations of established techniques (peephole LSTM are not new and should be cited) in the proposed methodology section. This could mislead the reader to think the LSTM and GRU variations in this section are new. This article should be cited when refering to peephole LSTMs: "Gers, Felix A., Nicol N. Schraudolph, and Jürgen Schmidhuber. "Learning precise timing with LSTM recurrent networks." Journal of machine learning research 3.Aug (2002): 115-143."
The Turkish dataset was still not properly explained, namely how it was adapted from an English dataset. The authors provide the repository with the code used, although I also could not find the questions corpus there, only a link to the Turkish wikipedia dump.
The lines that the authors provide in their response to not match the PDF I have, at least lines 639-648 on the track changes file are something else. Anyway it would be nice if there was a better explanation of the difficulty in processing the Turkish language, such as lemmatization, for those that are unfamiliar with this language. It was interesting to know that it does not have grammatical gender or noun classes but then the techniques used do not seem to differ from the ones used for English.
Could you please check the reviewer comments?
All the best
[# PeerJ Staff Note: Please ensure that all review comments are addressed in a rebuttal letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. It is a common mistake to address reviewer questions in the rebuttal letter but not in the revised manuscript. If a reviewer raised a question then your readers will probably have the same question so you should ensure that the manuscript can stand alone without the rebuttal letter. Directions on how to prepare a rebuttal letter can be found at: https://peerj.com/benefits/academic-rebuttal-letters/ #]
[# PeerJ Staff Note: The review process has identified that the English language must be improved. PeerJ can provide language editing services - please contact us at [email protected] for pricing (be sure to provide your manuscript number and title) #]
In this manuscript, the authors investigate three-to-five deep learning approaches based on 10-fold cross-validation for question classification tasks in the Turkish language. Kindly suggestions:
Better to move the problem statement (lines 61-65, 78-80) to the right section related to the methodology of this study.
The equations 2-7 with regard to LSTM (long short term memory), and the equations 8-11 about GRU (gated recurrent unit), might contribute limited knowledge.
The setup of the section “METHODOLOGY & RESULTS” might appear a bit unreasonable. That is because the proposed approach and focused novelties should be further improved to facilitate the recognition of other scholars.
The first technique of Wored2vec might not have been well introduced in the context. Further clarification would be grateful.
It's hard to ignore that the section of experiments might not have been well structured. That is to say, the arrangement of this article ought to be further enhanced before the next submission.
The novelties of the presented study should be further highlighted in preparing the future version of this manuscript. That might be because innovation is the gold criteria to recognize the most promising study.
The authors have told us a story about their focus on the basis of their deep literature investigation.
The writing needs to be substantially and thoroughly improved and proofread so that it can properly deliver the messages of this work to the reviewers and the readers. There are scattered grammar issues and typos across this manuscript in its current form.
To list a few:
Line 20: There are... have been ... -> ... that have been ...
Line 44: ... and how to ... -> ... (becomes an open question.)
Line 68: ... ... to the credible classify of the answers -> ... credible classification of ...
Line 71: After categories...
Line 96: Most articles focus on English language has ... -> ... focused on ...
Line 123: stag -> stage
The related work part is kind of out-dated, for which the authors may refer to more recent works.
The descriptions of the experimental settings are missing, including the data preprocessing steps, and the training policies and hyper-parameter settings of the deep learning models.
The supplementary code files can not be open properly. You may use UTF-8 for the file encoding.
The publish and organization of a Turkish dataset is definitely meaningful and positive.
When saying ``there are several unique features in Turkish languages that make NLP challenging'', please explain it with more details as it's the main motivation of this work.
The whole manuscript should be revised because the quality of written language is very poor making it difficult to understand. It has many grammatical issues and repeated nonsensical text. Even the first sentence of the abstract is an example of this repetition: "Question classification is one of the important tasks for automatic question classification in natural language processing (NLP).". Throughout the text there are too many examples to list. Some technical terms used are also not very clear, for example, the authors say that Turkish is "strongly inflective language" but they also say that it is agglutinative, so this issue should be clarified for the readers.
I would also advise to dismiss most of the text about Word2vec and deep learning models since these are established and widely used algorithms. Instead, more examples about the problem at hand (Turkish question classification) should be given.
The manuscript also has an organization issue: "DEEP LEARNING APPROACHES" starts with an overview of deep learning models but then starts describing the proposed model. Then on "METHODOLOGY & RESULTS", the authors provide a full explanation of the word2vec algorithm, as well as experimental details (but no results since the next section is called "Experimental Results").
The experimental design seems to be an application of existing architectures to turkish question classification. I was unable to understand what adaptations were made to work with Turkish questions. Furthermore very few details are given about the dataset. A dataset was created by translating an english language dataset, but it was not clear how and by whom it was translated and if this dataset is or will be made publicly available. As neither the code or data used to train the models is provided, it would be difficult to replicate the results.
The authors provide a comparison between all the architectures considered and different embeddings. They claim that in comparison to english questions, they have lower results due to "an absence of effectiveness lemmatization methods for Turkish Language compared to English language.". However this is the first that they mention lemmatization so I don't know why and how this would impact the results.
This manuscript presents a deep learning approach to question classification applied to turkish questions. The authors compare several deep learning architectures and they mention the challenge that is doing this type of task in Turkish texts. They provide a strong related work section on this task, and describe the models used in detail, however due to language issues most of the manuscript is difficult to understand.
All text and materials provided via this peer-review history page are made available under a Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.