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Context:

I have documents with reviews of articles that have the following structure:

  • Introduction: a description of the review, dates and metadata that will be discarded. (avg~180 words, std~30 words)
  • Loop with:
    • Reference: the reference article to review. A single review can talk about multiple articles. (avg~8 words, std~2 words)
      • Subject: topic of the following comments. There can be more than one subject for the following comments. (avg~7 words, std~3 words)
        • Comment: atomic comment about the article. There can be multiple comments per subjects. (avg~45 words, std~30 words)
  • Final: a brief summary of the review and other info that will be discarded. (avg~165 words, std~40 words)

I've to identify orderly the different chunks of references, subjects and comments. I've already designed a pipeline, and have a dataset to test and train my models, I'm using BIO tags for chunking as output, but I can adapt the pipeline to use BMEWO (BILOU).

I'm searching for a similar problem dataset, to compare how my pipeline is performing against the best metrics accomplished in that problem.

I've already searched in:

https://archive.ics.uci.edu/ml/datasets.html

https://www.kaggle.com/datasets

Until now the closest dataset I've found is the CoNLL for named entity recognition or grammar tagging(noun phrases, prepositional phrases, verb phrases).

But that problems seems very different from mine:

  • Small length per chunk in relation with my problem.
  • The length of every chunk class are similar, In my case the size of the comments are very different than the subjects and references.
  • The outside tag could be everywhere while in my problem only can be at the beginning or the end.
  • These problems seems more focus on short grammatic structure instead of long sequence meaning.

I'm searching with the following keywords: chunk, long-sequence, text. I'm not sure if the word chunk could be misleading.

Where can I keep searching datasets? What keywords can I use to improve my search? There is a similar problem in which I could try to transform mine to test against?

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closed as off-topic by Stephen Rauch, Toros91, Aditya, Icyblade, Sean Owen Jun 2 '18 at 0:36

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