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lecturebank's Introduction

LILY LectureBank

This is the repo for the LectureBank Corpus, with all batches and updates.

Note that we also have a few works using part of the corpus, you can find more details in the LB-Paper folder.

Meta Data

data-versions

lb*.tsv: data with different versions.

ID, Instructor, Title, Topic, URL, Venue, Year

  • ID: Id of each line.
  • Instructor: The author name(s).
  • Title: File tile.
  • Topic: The Topic Number, check taxonomy.csv for topic name.
  • URL: Online URL.
  • Year: Year of the course.
  • Venue: Name of the university, or GitHub.

We went through a URL check on May, 2022, here are the valid resource numbers:

  • 1020 lb1.tsv
  • 308 lb2.tsv
  • 3564 lb3.tsv
  • 3136 lb4.tsv
  • 1321 lb5.tsv
  • 397 lb6.tsv

NOTE: we combined all five batches of LectureBank, and remove duplicates and invlaid urls. All data can be found in alldata.tsv with a total number to be 7499.

Taxonomy

NLP taxonomy release. In the file taxonomy.csv, we include the taxonomy with 320 topics in a tree structure. The topic ID for each topic shows the parent node. For example, 233 (Relation Extraction) has a parent node to be 23 (Part of Speech Tagging), and topic 23 has its parent node to be 2 (Language Modeling, Syntax, Parsing).

  • Topic ID: Id of topic.
  • Topic: topic name.

You can find how this was created in our paper CLICKER: A Computational LInguistics Classification Scheme for Educational Resources.

Other resources

Please visit our website AAN.how.

lecturebank's People

Contributors

dragomirradev avatar irenezihuili avatar lutz-100worte avatar mistobaan avatar yixinl7 avatar

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lecturebank's Issues

LectureBank2 dataset

Can the plain text of LectureBank2 be provided? Most of the URL links are no longer valid.

How to get the concept representation by Doc2Vec?

Hi,
I read your paper, and I'm wondering how you get the concept representation with Doc2Vec? First, each slide may contains multiple concepts. Second, each concept may be contained in multiple slides. How do you deal with these issues?

Thank you!

R-VGAE data

Hi, your R-VGAE is awesome!
I'm trying to reproduce your work, but you don't seem to have released the training data, if you are not convenient to disclose the data, can you please share the script used to process the data?

datasets and code

hi, thanks for the code.
In the paper R-VGAE, Sime-supervised uses concept-concept edges, but in the code there is only document-concept, document-document adjacency matrix.
if args.ds.startswith('tf'): if args.labels == 'y': adj_cd, adj_dd, features, tags_nodes = my_load_data_tfidf_semi(args.wmd) else: adj_cd, adj_dd, features = my_load_data_tfidf(args.wmd)

  1. I want to know where concept-concept edge is used?
  2. When using TF-IDF as the embedding feature, how is the feature of the concept obtained?
  3. I know the tags of concepts are 0-321, what are the tags of documents?

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