Abstract | ||
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Characterizing the content of a technical document in terms of its learning utility can be useful for applications related to education, such as generating reading lists from large collections of documents. We refer to this learning utility as the of the document to the learner. While pedagogical value is an important concept that has been studied extensively within the education domain, there has been little work exploring it from a computational, i.e., natural language processing (NLP), perspective. To allow a computational exploration of this concept, we introduce the notion of of documents (e.g., Tutorial and Survey) as an intermediary component for the study of pedagogical value. Given the lack of available corpora for our exploration, we create the first annotated corpus of pedagogical roles and use it to test baseline techniques for automatic prediction of such roles. |
Year | DOI | Venue |
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2017 | 10.18653/v1/w17-5012 | BEA@EMNLP |
DocType | Volume | Citations |
Journal | abs/1708.00179 | 1 |
PageRank | References | Authors |
0.36 | 9 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Emily Sheng | 1 | 6 | 1.62 |
Premkumar Natarajan | 2 | 874 | 79.46 |
Jonathan Gordon | 3 | 65 | 9.29 |
Gully A. P. C. Burns | 4 | 172 | 12.17 |