PONE-D-20-29449

Hierarchical Lifelong Topic Modeling Using Rules Extracted From Network Communities

PLOS ONE

Dear Dr. Khan,

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Diego Raphael Amancio

Academic Editor

PLOS ONE

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: No

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper presents a hierarchical lifelong topic model called NHLTM. This new model combines lifelong learning, with network community detection and topic modelling.

Overall I found that the manuscript was well organized and the contributions were clear. There were several non-native grammar errors. I probably did not catch them all, but I did upload my notes to this review. Please look at my ink-notes for detailed comments.

My broad remarks are as follows:

1. Reporting raw likelihood values is not normal and not meaningful. Please do not report -856, etc. as results. Just say that it was improved.

2. Section 1 and section 2 are otherwise well organized with only minor typographical and grammatical errors.

3. In section 4 you talk about treating the text corpora as a graph. There exists the hierarchical document topic model (HDTM) which does something similar here that you could look at.

4. The input of Alg 1, does it require preprocessing of the whole data each time? The input looks like it does.

5. In general, Section 4 is too verbose. You do not need to describe each line of Alg 1. Alg 2, Alg 3, and Alg 4 are very straightforward and can just be described in prose. Likewise Eq 5 and Eq 6 are well known functions. Just say normalized Laplacian and Fiedler Vector. You don't need to derive it. Any reader will know what those things are. Eq 7 is called "conductance cut" and is well known too - there is no need to describe these things. I think that Section 4 could be reduced to 3-4 pages total.

6. In section 5, you only perform experiments on a single dataset. This is not enough for conclusive study. There exist enormous datasets for study. You need to perform a much more thorough analysis with many datasets.

7. In Table 5, what is the hexadecimal data supposed to show? Why not just represent things as ints? What is Comm Sr. No. column?

8. Fig 2 us supposed to have some nodes with a grey background, but my printout does not have anything in grey.

9. In Section 5, where do you test the "lifelong" portion of NHLTM?

10. Whats the purpose of Table 6? And on page 23 generally, raw values are just not informative.

11. Fig 3 has the y-axis flipped. It goes from largest on the bottom to smallest on the top. This is quite abnormal.

12. Table 7 should be a figure.

13. Conclusions are not strong. What did we learn in this paper? What are the limitations of NHLTM? What is the future work. Expand this section please.

Overall I think the technical contribution of this paper is ok, but the experiments are severely lacking. Significant additional work is needed before this paper can be accepted.

Reviewer #2: While interesting, I believe this paper needs major revisions for improved clarity and a more succinct narrative.

- What do you mean by rules? You should present a clear example and definition very early. In one case, it appears that a rule consists of a correlation of two words, but a subsequent equation seems to contradict this.

- Am I right in thinking you used raw frequency? In "classic" approaches to topic modeling, raw frequency is rarely used. It's more common to see something like positive pointwise mutual information (PPMI). Were alternatives to raw frequency explored? If so, why wasn't this reported?

- Moreover, typically, term term matrices have a zero diagonal.

- I recommend that you avoid using "Extract communities using Algorithm 3" when you can instead reference your favored technique by name.

- Your tables and figures all warrant real captions so that they can be digested on their own.

- Word embeddings have revolutionized natural language processing. Recent approaches to topic modeling that have appeared in venues such as TACL and various ACL conferences have all involved word embeddings to some degree. Why is no discussion or comparison made here? While this might require a second evaluation on another dataset, I think it's necessary.

- Speaking of recent approaches, how do neural topic models (ex. VAE-based) compare to your approach? For example, a comparison to Gupta et al's Neural Topic Modeling with Continual Lifelong Learning (ICML 2020) would be nice.

- How does the approach work with different community detection algorithms? Why did you select this particular one without any comparison?

- How are you evaluating the lifelong aspect of your algorithm?

- AMC-M, AMC and OAMC

- Are the abbreviated forms of these acronyms introduced anywhere?

- Table 5: this should be reformatted to make it more obvious which communities are "close".

- Please make your code available for reproducibility and future comparisons. Doing so is now standard practice. Ideally, I would like to see how you compared the different models.

- Though not a serious problem, there are numerous typographical errors (repeated words, inconsistent formatting, agreement errors, missing determiners, etc.).

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Reviewer #1: No

Reviewer #2: No

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Attachments

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Submitted filename: 0658_001.pdf

PONE-D-20-29449R1

Hierarchical Lifelong Topic Modeling Using Rules Extracted From Network Communities

PLOS ONE

Dear Dr. Khan,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

The reviewer pointed out that major issues have been addressed. However, language should be checked in order to improve readability. 

==============================

Please submit your revised manuscript by Jul 23 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Diego Raphael Amancio

Academic Editor

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The author has addressed all of my comments. The new parts of the paper need a thorough proofread - there are minor grammatical issues, but nothing major.

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PONE-D-20-29449R2Hierarchical Lifelong Topic Modeling Using Rules Extracted From Network CommunitiesPLOS ONE

Dear Dr. Khan,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Feb 13 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Diego Raphael Amancio

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #3: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Grammatical changes appear to be satisfactory. I have no further comments to make regarding the revision of this paper.

Reviewer #3: This paper proposes an interesting approach for topic modeling. More specifically, the authors put together concepts of NLP and network science to develop a topic modeling approach. Since the author already answered the other reviewers, I have only some points concerning the text.

- Since the method involves techniques from different areas, I believe it would be better to explain in more detail some basic concepts. For example, how to calculate and interpret "topic coherence" and "Von Neumann entropy". Note that these concepts are simple, but researchers only from one of the areas involved in this work would not know these concepts.

- On page 12, the authors describe that a co-occurrence graph is used. However, it would help the reader to describe more characteristics of this graph. More specifically, if the edges are directed and/or weighted and how it is determined.

- Topic modeling based on communities of complex networks have been studied in:

Silva FN, Amancio DR, Bardosova M, Costa LD, Oliveira Jr ON. Using network science and text analytics to produce surveys in a scientific topic. Journal of Informetrics. 2016 May 1;10(2):487-502.

Furthermore, a multi-scale extension of this method was proposed in:

Ceribeli C, de Arruda HF, da Fontoura Costa L. How coupled are capillary electrophoresis and mass spectrometry?. Scientometrics. 2021 May;126(5):3841-51.

Considering that both papers are significantly related to the paper, I think these references could be considered.

- In step 5 of section 4.2, it is written: "The α as the smoothing factor." I think that it should be: "α is the smoothing factor." Furthermore, in "Moreover, the β is used as a smoothing factor", I think there is no "the".

**********

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Reviewer #1: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Hierarchical Lifelong Topic Modeling Using Rules Extracted From Network Communities

PONE-D-20-29449R3

Dear Dr. Khan,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Jerry Chun-Wei Lin

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for addressing my reviews. The current version of the paper does not pose any obvious flaws according to the PLOS ONE publication model.

**********

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Reviewer #1: No